1. Introduction
The extensively studied faulttolerant consensus problem (Pease et al., 1980) is a fundamental building block of many important distributed computing applications (Lynch, 1996). The FLP result (Fischer et al., 1985b) states that it is impossible to achieve exact consensus in asynchronous networks where nodes may crash (exact consensus requires nonfaulty nodes to reach an agreement on an identical value). The FLP impossibility result led to the study of weaker variations, including approximate consensus (Dolev et al., 1986). With approximate consensus, nonfaulty nodes only need to output values that are within of each other for a given . Practical applications of approximate consensus range from sensor fusion (Benediktsson and Swain, 1992) and load balancing (Cybenko, 1989), to natural systems like flocking (Vicsek et al., 1995) and opinion dynamics (Hegselmann and Krause, 2002). The feasibility of achieving consensus depends on the type of faults considered in the system. The literature has mainly focused on crash and Byzantine faults, the latter being the worst case since the misbehavior of faults may be arbitrary. In this work, we focus on the asynchronous Byzantine approximate consensus problem under the existence of at most faults.
Another important parameter affecting the feasibility is the topology of the underlying communication network in which nodes represent participants that reliably exchange messages through edges. The relation between network topology and feasibility in undirected networks was studied shortly after the introduction of the respective problems (e.g., (Lynch, 1996; Dolev, 1982)). For , connectivity of the network and upper bound on the number of faults, Table 1 summarizes the wellknown necessary and sufficient topological conditions for achieving exact consensus and approximate consensus in various settings where is undirected. In undirected networks, satisfying the necessary graph conditions in Table 1 also implies feasibility of reliable message transmission (RMT) (cf. (Dolev et al., 1993)), which can be exploited to simulate algorithms designed for complete networks.
Crash fault  Byzantine fault  

Synchronous system
(exact consensus) 
and
(Lynch, 1996) 
and
(Dolev, 1982) 
Asynchronous system
(approximate consensus) 
and
(Lynch, 1996) 
and
(Fischer et al., 1985a; Abraham et al., 2004; Dolev et al., 1993) 
The study of consensus in directed graphs is largely motivated by wireless networks wherein different nodes may have different transmission range, resulting in directed communication links. While the necessary and sufficient conditions for undirected graphs have been known for many years, their generalizations for directed graphs appeared only after 2012, e.g., (Tseng and Vaidya, 2012, 2015; LeBlanc et al., 2013; Vaidya et al., 2012). This is mainly due to the fact that no direct relation appears between reliable message transmission and consensus in directed graphs.
As Table 2 summarizes, for directed graphs, Tseng and Vaidya (Tseng and Vaidya, 2015, 2012) obtained necessary and sufficient conditions for solving consensus in the presence of crash faults in synchronous and asynchronous systems both. However, they were able to obtain such conditions for Byzantine faults only for synchronous systems. The determination of a tight condition for the asynchronous Byzantine model remains open since 2012. This paper closes this gap in the results. We identify a family of new conditions which we prove equivalent to the ones obtained in (Tseng and Vaidya, 2012, 2015), offering an important intuition, which essentially leads to the answer of this open question. Our condition family consists of 1reach, 2reach and 3reach conditions, which are later defined in Section 2.^{1}^{1}1The general kreach condition family, presented in the appendix, encompasses conditions 1reach, 2reach, 3reach and may be of further interest. Results from (Tseng and Vaidya, 2012, 2015) imply that the 3reach condition is tight for exact Byzantine consensus in synchronous systems. A key contribution of this paper is to show that the 3reach condition is also necessary and sufficient for asynchronous Byzantine consensus in directed graphs.
Crash fault  Byzantine fault  

Synchronous system
(Exact consensus) 
1reach condition (see Section 2)
Tseng and Vaidya 2015 (Tseng and Vaidya, 2015) 
3reach condition (see Section 2)
Tseng and Vaidya 2015 (Tseng and Vaidya, 2015) 
Asynchronous system
(Approximate consensus) 
2reach condition (see Section 2)
Tseng and Vaidya 2012, 2015 (Tseng and Vaidya, 2012, 2015) 
3reach condition (this paper)
open problem since 2012 
Essentially, obtaining the tight graph conditions for directed graphs is much more difficult than the undirected case, since consensus may be possible even if reliable message transmission (RMT) is not possible between every pair of nodes. This is unlike the case of undirected graphs, as observed previously. For instance, Figure 1(a) presents an undirected network, where synchronous exact Byzantine consensus is possible for . In this graph, allpair RMT is possible, since allows any pair of nodes to communicate through at least disjoint paths. Note that removing any edge will reduce , which will make both RMT and consensus impossible. Such an allpair RMT is not necessary in directed graphs. In particular, Figure 1(b) shows a network that satisfies the 3reach condition (stated later in Section 2) – this network includes two cliques, each containing 7 nodes, and eight additional directed edges as shown (edges within each clique are not shown in the figure). Observe that there are pairs of nodes (e.g., and ) that are connected via only disjoint paths. Clearly, allpair RMT is not feasible in this case but consensus can still be achieved, as shown by (Tseng and Vaidya, 2012) and our results. The difficulty posed by directed graphs is further compounded by asynchrony. In this work, we show that the 3reach condition is necessary and sufficient for asynchronous Byzantine approximate consensus in directed graphs – note that this condition is identical to that proved by Tseng and Vaidya (Tseng and Vaidya, 2015) for synchronous Byzantine exact consensus.
Related work
Additional related work includes studies of the special class of iterative algorithms, which only utilize local knowledge of the network topology and employ local communication between nodes. A tight condition for iterative approximate Byzantine consensus has been presented in (Vaidya et al., 2012; LeBlanc et al., 2013). A family of tight conditions for approximate Byzantine consensus under the more general class of hop iterative algorithms has been presented recently in (Su and Vaidya, 2017) but is restricted to synchronous systems. The feasibility of asynchronous crashtolerant consensus with respect to the hop iterative algorithms has been considered in (Sakavalas et al., 2018). A series of works (Litsas et al., 2013; Pagourtzis et al., 2017a, b) studies the effects of topology knowledge on the feasibility of RMT, and consequently exact consensus in undirected networks with Byzantine faults.
2. Preliminaries and Main Result
For the approximate consensus problem (Dolev et al., 1986), each node is given a realvalued input, and the algorithm needs to satisfy the three properties below.
Definition 0 ().
Approximate consensus is achieved if the following conditions are satisfied for a given .

Convergence: the output values of any pair of nonfaulty nodes are within of each other.

Validity: the output of any nonfaulty node is within the range of the inputs of the nonfaulty nodes.

Termination: all nonfaulty nodes eventually output a value.
System Model
We consider an asynchronous messagepassing network. The underlying communication network is modeled as a simple directed graph , where is the set of nodes, and is the set of directed edges between the nodes in . Node can reliably transmit messages to node if and only if the directed edge . Each node can send messages to itself as well; however, for convenience, we exclude selfloops from set . A link is assumed to be reliable, but the message delay is not known a priori.
In the system, at most nodes may become Byzantine faulty during an execution of the algorithm. A faulty node may misbehave arbitrarily. The faulty nodes may potentially collaborate with each other.
New Graph Conditions
Hereafter, we will use the notation to denote the complement of set . The subgraph of induced by node set will be denoted by . For a given node set , we now define the reach set of node under , originally introduced in (Tseng and Vaidya, 2015).
Definition 0 (Reach set of under ).
For node and node set , define
Observe that a node belongs to if is reachable from in the subgraph of induced by node set . Trivially, is in . With the definition of a reach set, we introduce the 1reach, 2reach and 3reach conditions referred in Section 1. Intuitively speaking, in the definitions below, the sets , , represent potential sets of faulty nodes; thus, these sets are chosen to be of size . In the following, recall that denotes the set .
Definition 0 (Reach Conditions).
We define three conditions:

1reach: For any such that and any nodes , we have

2reach: For any nodes and any node subsets , such that , , and , we have

3reach: For any nodes and any node subsets , , such that , , and , we have
It is easy to verify that in a clique, 1reach, 2reach, and 3reach are equivalent with , and respectively. Details can be found in Appendix A.
Main Results
As noted previously, Tseng and Vaidya (Tseng and Vaidya, 2015) obtained necessary and sufficient conditions enumerated in Table 2. We have shown, in Appendix A, that each of their conditions to be equivalent to a respective reach condition in Definition 3 above. In particular, based on the results in (Tseng and Vaidya, 2015), we can prove Theorems 4, 5 and 6 below. These results are not used to prove our main results; hence, we defer the presentation of the original conditions in (Tseng and Vaidya, 2015) and the equivalence proofs to Appendix A.
Theorem 4 ().
Synchronous exact consensus is possible in network in the presence of up to crash faults if and only if satisfies 1reach condition.
Theorem 5 ().
Asynchronous approximate consensus is possible in network in the presence of up to crash faults if and only if satisfies 2reach condition.
Theorem 6 ().
Synchronous exact consensus is possible in network in the presence of up to Byzantine faults if and only if satisfies 3reach condition.
Main Result
Theorem 7 ().
Asynchronous approximate consensus is possible in network in the presence of up to Byzantine faults if and only if 3reach is satisfied.
This result solves the open problem in Table 2 in Section 1.
Proving the main result: The sufficiency of the 3reach condition for asynchronous Byzantine approximate consensus is demonstrated constructively in Section 4, using Algorithm 1 for achieving this goal. The necessity of the 3reach condition for asynchronous Byzantine approximate consensus follows by standard indistinguishability arguments; the proof is deferred to Appendix B.
Technique Outline: Our result generalizes the result of (Abraham et al., 2004), which shows the sufficiency of condition for asynchronous Byzantine approximate consensus in a clique. Note that the condition coincides with the tight condition for the synchronous case (cf. Table 1). For directed graphs, we show that 3reach is the tight condition for both the synchronous and asynchronous cases. Condition 3reach states that there exists a node that has (i) a directed path to node in the subgraph induced by the node subset , and also (ii) a directed path to node in the subgraph induced by the node subset . This “source of common influence” for any pair of nodes is crucial for achieving consensus. We outline two techniques used towards our generalization, since they may provide useful intuition for other faulttolerant settings.
Maximal Consistency: We simplify the ReliableBroadcast subroutine of (Abraham et al., 2004) by essentially replacing several rounds of communication between nodes with flooding. Even in a clique, communication rounds can be simulated by flooding through propagation paths ^{2}^{2}2Observe that the definition of a path also applies in a clique network. of length at most . The receiver of all these propagated messages can then detect the existence of faults in certain propagation paths if the propagated values are inconsistent (i.e., values from different paths do not match). The technique appears in the use of the MaximalConsistency condition in Algorithm 1; this simple condition provides similar properties as ReliableBroadcast of (Abraham et al., 2004).
Witness node: The witness technique used in (Abraham et al., 2004) relies on the fact that for any pair of nodes, there is a nonfaulty witness node which provides them with enough common information. The existence of an analogous nonfaulty witness for directed networks is implied by the 3reach condition. Intuitively, even if two nodes “suspect” different sets to be faulty, the existence of a common nonfaulty witness guarantees the flow of common information to both. Guaranteeing that all pairs of nonfaulty nodes gather enough common values while ensuring that nonfaulty nodes with wrongly suspected faulty set are always able to proceed appeared to be the most challenging part of the proposed algorithm. This technique appears in how each node verifies the messages that it has received at line 1 in Algorithm 1. Generally speaking, a node tries to collect as many “verified messages” as possible while it cannot wait for messages that might never arrive (i.e., message tampered by faulty nodes).
3. Useful Terminology
Recall that directed graph represents the network connecting the nodes in the system. Thus, . We will sometimes use the notation to represent the set of nodes in graph . In the following, we will use the terms edge and link interchangeably. We now introduce some graph terminology to facilitate the discussion.

A path is represented by an ordered list of vertices. In particular, is a directed path comprising of nodes and directed edges , where .

and , will be used to denote the initial node and terminal node of a path .

A path is a path with and .

Operation denotes the concatenation of path with node assuming that . Analogously, if , then denotes the concatenation of paths and .

Redundant path: a path is a redundant path if for some simple paths and ( and have no cycles) and one of may be empty. Note that a redundant path may contain cycles and its length is upper bounded by .

The set of all redundant paths in graph (defined above) will be denoted as .

Fully nonfaulty path: a path consisting entirely of nonfaulty nodes.

paths: given a set and a node , an path is a path with and .
When convenient, we will interpret a path as the set of nodes in the path. The next few definitions use this interpretation for a node set and paths , .

will denote the intersection .

We will say that if .

By , we will denote the node intersection of paths and .
Definition 0 (cover of a path set).
For a set of paths , a node set is a cover of , if , and
.
Definition 0 (Reduced Graph).
For graph , and sets , such that , reduced graph has set of vertices , and the set of edges is obtained by removing from all the outgoing links at each node in . That is,
Definition 0 (Source Component).
For graph , and sets , such that , source component is defined as the set of those nodes in the reduced graph that have directed paths to all the nodes in .
By definition, the nodes in form a strongly connected component in . The source component has other desirable properties that will be introduced when we prove the correctness of our algorithm later.
4. Asynchronous approximate consensus in directed networks
We next present an algorithm for approximate Byzantine consensus in asynchronous directed networks. The algorithm is optimal in terms of resilience, meaning that it matches the impossibility condition of the problem, i.e., the algorithm works in any graph that satisfies 3reach. Our solution is inspired by the asynchronous approximate consensus algorithm of (Abraham et al., 2004) as explained in Section 2. However, the tools used in the algorithm of (Abraham et al., 2004) prove highly nontrivial to generalize in the case of a partiallyconnected directed network. This is due to the constraint of directed edges. In complete graphs considered in (Abraham et al., 2004), information can flow both directions, and each node can use the same rule to collect information. In the case of directed networks, information may only be able to flow in one direction. We need to devise new tools for nodes to exchange and filter values so that enough common information is shared between any pair of nonfaulty nodes.
Outline of the algorithm
In our algorithm, each node maintains a state value , which is updated regularly, with denoting the realvalued input of node . Value represents the th update of the state value of node ; we will also refer to it as the state value of in (asynchronous) round . Observe that in asynchronous systems, updates the value every time it receives enough messages of a certain type (i.e., an eventdriven algorithm), thus creating the sequence . The th value update of a node may happen at a different real time than the respective update of another node .
The proposed algorithm is structured in two parts presented in Algorithm 1: Byzantine Witness (BW) and Algorithm 3: FilterandAverage (FA). Algorithm BW intuitively guarantees that all nonfaulty nodes will gather enough common information in any given (asynchronous) round. The value update in each round is described in Algorithm FA, where we propose an appropriate way for a node to filter values received in Algorithm BW and obtain the state value for next round as an average of the filtered values. Each node needs to filter potentially faulty values to guarantee validity.
4.1. Algorithm Preliminaries
The proposed algorithm utilizes the propagation of values through certain redundant paths (defined in Section 3). We then describe tools for handling information received by a node through different paths
Messages and Message Sets.
In our algorithm, the messages propagated are of the form where is the propagated value and corresponds to the (redundant) path through which the value is propagated, i.e., its propagation path. For a message , we will use the notation and to denote the propagated value and propagation path, respectively. For simplicity, we will also use the terminology receives value from whenever node receives through some path initiating at node . A message set is a set of messages of the form where is the value reported though propagation path . Given , we will use to denote the set of all propagation paths in , i.e.,
As defined below, given a node set and a message set , the exclusion of on consists of the messages of that are propagated on paths that do not include any node in .
Definition 0 (Exclusion of message set).
Given a message set and , the exclusion of on is the set
The notions of a consistent message set and full message set, presented below, are used to facilitate fault detection. A message set is consistent if all valuepath tuples initiating at the same initial node report the same value.
Definition 0 (Consistent message set).
A message set is consistent if
Given a consistent message set , if and , then we define . That is, for a node that appears as an initial node of a path in , denotes the unique value corresponding to . Note that the value is guaranteed to be unique owing to the the definition of a consistent message set.
We say that the received message set is a full message set for , whenever a node receives messages from all possible incoming redundant paths excluding node set . The formal definition follows.
Definition 0 (Full message set).
Given and , a message set is full for if
4.2. Algorithm Byzantine Witness (Bw)
The Byzantine Witness (BW) algorithm, presented as Algorithm 1, intuitively guarantees that all nonfaulty nodes will receive enough common state values in a specific asynchronous round of the algorithm; eventually, this common information guarantees convergence of local state values at all nonfaulty nodes. The algorithm is eventdriven. That is, whenever a new message is received, each node checks whether a certain set of conditions are satisfied, and whether it should take an action. (In particular, Line 6, Line 8, Line 10, and Line 12 of Algorithm 1 will be triggered upon receipt of a new message.)
Parallel executions.
For the sake of succinctness, we present a parallel version of Algorithm BW; the algorithm makes use of parallel executions (threads) for any potential fault set . Note that there are exponential number of threads. In the parallel thread for set , a node “guesses” that the actual fault set of this execution is , and checks for inconsistencies to reject this guess. Observe that in lines 11 of Algorithm BW, the usage of a shared boolean variable guarantees that a node will proceed to the next round during at most one parallel execution; we will later prove that there always exists such a parallel execution that proceeds to the next round. For each round , during this unique parallel execution, the node will execute Algorithm FilterandAverage (FA), presented as Algorithm 3, through which the value is updated.
Suppose that a node’s parallel thread for set proceeds to the next round. It is possible that is not the actual faulty set. Our algorithm is designed in a way that even if the guess is incorrect, the node is still able to collect enough common values and make progress. Moreover, the parallel thread for set , where is the actual fault set, is guaranteed to make progress at every nonfaulty node.
Atomicity
Algorithm BW uses the shared variables and ; includes all values received by node and is updated whenever receives a new flooded value while indicates if a parallel thread has proceeded to the next (asynchronous) round. For certain parts of the algorithm we need access to shared variables to be atomic, i.e., reads and writes to shared variables can be performed only by a parallel thread at a time. For clarity of the latter, we make use of the functions lock() and unclock() which indicate the parts of the code performed in an atomic fashion.
We next describe a flooding subroutine used to propagate state values throughout redundant paths in the network.
RedundantFlood (Redundant Flood) algorithm
In the beginning of each asynchronous round, in algorithm 1, all nodes will flood their values throughout the network. The difference between RedundantFlood algorithm and the standard flooding is that each flooded message is propagated through any redundant path in the network, not just through simple paths. The details of the algorithm are deferred to the Appendix E.
FIFO flooding of messages
During the execution of the algorithm, we will employ the FIFO Flood and FIFO Receive procedures which ensure that the order of messages sent from a sender is preserved during reception of these messages by any receiver, when the propagating path is fully comprised of nonfaulty nodes. For the correctness of our algorithm, we only need to FIFOflood messages through simple paths in the network.^{3}^{3}3It is possible to use RedundantFlood everywhere. For efficiency, we only use RedundantFlood to propagate state values at the very beginning of each round. Thus, a node will propagate a message during FIFOFlood, only if the resulting propagation path does not contain any cycle. We present a highlevel description of the FIFO Flood and FIFO Receive procedures in the Appendix F.
Algorithm BW: Pseudocode
Algorithm BW is presented in Algorithm 1. We stress that Algorithm BW is executed for each asynchronous round . Thus, all messages sent in round will be tagged with corresponding round identifier . For simplicity, we omit round numbers in the presentation and the analysis of the algorithm. The properties of Algorithm BW are proved hold for any specific asynchronous round . For brevity, the pseudocode does not include the termination condition. We defer the discussion on termination to Section 4.6.
Function
We first remind the reader that denotes the source component of reduced graph as defined in Definitions 2, 3. Observe that due to function called in line 1, a node essentially waits to receive additional messages to the ones it received upon considering possible faulty set (during the parallel execution for ) before it proceeds to update its value through Algorithm FilterandAverage. Intuitively, for some received message , waits for the confirmation of the values in through enough redundant paths from a source component. We will later prove that if message is not faulty (i.e., tampered) then will eventually be able to “confirm” the values in . For the sake of simplicity, whenever the function at node is true for some given , we will simply state that condition is satisfied.
4.3. Properties of Algorithm BW
In the following, we introduce some notions necessary for the analysis of Algorithm BW. We first borrow the notion of propagation from (Tseng and Vaidya, 2012, 2015).
Definition 0 (Propagation between sets).
Given sets with , , set is said to propagate in to set if either (i) , or (ii) for each node , there exist at least nodedisjoint paths in the node subgraph of induced by node set , i.e., . We will denote the fact that set propagates in to set by .
Note that the disjoint paths implied in Definition 4, are entirely contained in . Next, observe that by Definition 3, for any sets it holds that . The following theorem is repeatedly used in our analysis; its proof is based on Corollary 2 in (Tseng and Vaidya, 2012) and the equivalence of 3reach condition with the condition in (Tseng and Vaidya, 2012) (proof in Appendix A). Intuitively, Theorem 5 below states that if 3reach is satisfied then there are at least disjoint paths, excluding nodes in , that connect a source component with any node outside the source component.
Theorem 5 ().
Suppose that graph satisfies condition 3reach. Then, for any and , such that , and hold.
Using the notions above, we will next show some important properties of Algorithm BW. As defined in line 1, we will say that node satisfies the MaximalConsistency Condition for node set if it receives the message set and is consistent and full for .
Lemma 6 ().
For any nonfaulty node , the MaximalConsistency condition will eventually be satisfied during a parallel execution for some set .
Proof.
Consider ’s parallel execution for set , where is the actual faulty set of the execution. If the MaximalConsistency condition has not been satisfied already, it will eventually be satisfied during the parallel execution for . This will happen since every node in behaves correctly and thus will eventually receive consistent values from all incoming paths in , i.e., will be consistent and full for . ∎
Lemma 7 ().
Consider two nonfaulty nodes that satisfy the MaximalConsistency condition on the same set . Let the message sets and be the sets that are used to pass MaximalConsistency condition at and , respectively. Then, the two sets contain the same unique value .
Proof.
We first prove that for each , both nodes and will receive a unique value corresponding to , contained in the respective sets and . Observe that for any and , by Theorem 5 and the fact that any source component is strongly connected, there exists a simple path in . Since is full for , will contain some value corresponding to . Note that this value might not be the value sent by , since the above simple path might contain some faulty node. Next, recall that we also require to be consistent. Therefore, the previously mentioned value contained in must be unique . The same argument applies to , too.
The 3reach condition implies the existence of a node for the actual faulty set . By definition, is nonfaulty and is connected to both through fully nonfaulty simple paths and respectively. By Theorem 5, either or there exist simple disjoint paths in . In both cases, there exists a simple path in . Note that there might be some faulty nodes in , since might not be the actual faulty node set.
This observation implies that in , there exist a redundant path and a redundant path such that the first part is identical in both paths. Note that the reach condition only implies that and are fully nonfaulty. Hence, it is possible that the value sent by node is , but the message(s) propagated through and are different. Since and are full; nodes and will receive some value from paths and , respectively. The value received by and must be identical. This is because (i) the two redundant paths have a common first part ; and (ii) and are fully nonfaulty by assumption. Let this value be (which may or may not equal to , the original value sent by ). Finally, since is consistent, all the other messages propagated through paths with and must also be , the value forwarded by . The same argument applies to . Thus, For each , there exists a common value in both and .
∎
Next we prove the main Lemma for the correctness of Algorithm BW. With FIFOReceiveall condition we refer to the condition stated in the event handler of line 1.
Lemma 8 ().
Consider a nonfaulty node such that the FIFOReceiveAll condition is satisfied at node for some parallel execution. If by the time the FIFOReceiveAll condition is satisfied, receives from a fully nonfaulty path with , then will eventually receive a message set such that the condition will be satisfied at node .
Proof.
First observe that since path is fully nonfaulty, is nonfaulty; also lines 1,1 of BW imply that since it propagates . Consider any with and any . Let be the actual faulty set of the execution. Note that since nonfaulty receives from a fully nonfaulty path with , node must have FIFOFlooded this message during the execution. By the 3reach condition of Definition 2, we have the following.
(1) 
This, for any , implies the existence of a fully nonfaulty simple path and a fully nonfaulty simple path in graphs and , respectively.^{4}^{4}4Set is not to be confused with the set during the parallel execution of which satisfies the FIFOReceiveAll condition. Set is arbitrary in the proof. Note that might even receive values from paths intersecting with in order to satisfy the condition during its parallel execution for . This might occur if is a wrong “guess” of the actual fault set. We consider the following two cases for ,

Case I: .
We first prove the following key claim.
Claim 1:
Both and receive an identical value from node .
Proof of Claim: Since is strongly connected, there exists a simple path in graph . This implies the existence of the redundant path in . Recall that by assumption, is nonfaulty and FIFOFloods . This means that has received a unique value through all redundant paths in , particularly through path . Note that since might contain some faulty nodes, might be different from the value originally sent by node . Observe that there also exists the redundant path in which will eventually propagate the same value to . This is because is fully nonfaulty and the initial part is identical in both and .
Claim 2:
Node will eventually receive from a set of paths with no cover .
Proof of Claim: Recall that Claim 1 holds for any , with . Then will eventually receive from all redundant paths for any being a path with . This is because all these paths are fully nonfaulty and the initial part propagates as implied by Claim 1. The set of all these paths does not have an cover . If there was such an cover , this would contradict Equation (1) because it would mean that no fully nonfaulty path would exist in .^{5}^{5}5Observe that if and receives from a single path that entirely consists of nodes in , then no cover exists for this path. This is because by definition.

Case II: .
Theorem 5 implies that there exist simple disjoint paths in . This together with the observation that is strongly connected imply the existence of simple paths which trivially do not have an cover . Similarly with the previous case, since FIFOFloods , it must have received the same value from all redundant paths . Since and is fully nonfaulty, one of the paths will also eventually propagate value to .
Using the same argument for Claim 2 in Case I, will eventually receive from a set of paths with no cover .
In both cases, any such value received by will be consistent with values propagated by , and thus will eventually satisfy the condition.
∎
In the following, we will consider the case where a node executes Algorithm FilterandAverage through line 1, during its parallel execution for set . In this case, has already satisfied the MaximalConsistency condition corresponding to as well as the conditions for all messages it has received by the time it satisfied the FIFOReceiveAll condition. Intuitively, this means that has received redundant messages corresponding to “suspicious sets” by the time it executes FilterandAverage. Note that there exists only one such parallel execution during which FilterandAverage is executed; this follows easily from the atomic updates of shared variable and lines 11 of Algorithm BW. We will use the following notion in our proofs.
Definition 0 (Informed node).
A node that executes FilterandAverage during its parallel execution for a set is informed about set if , or has satisfied the condition after receiving message from a fully nonfaulty path with .
Theorem 10 ().
Any nonfaulty node will eventually execute FilterandAverage during a parallel execution for a set .
The proof of Theorem 10 relies on the observation that algorithm FilterandAverage will be executed during parallel execution for actual fault set if not during any other parallel execution. The full proof is presented in Appendix D.
Theorem 11 ().
Let any pair of nonfaulty nodes which execute FilterandAverage during their parallel executions for sets and , respectively. Then, both nodes and will be informed about a node set , where , and will both receive a common value for each . More specifically, each value will be the unique value corresponding to node that node received by the time it satisfied its MaximalConsistency condition.
Proof.
Theorem 10 implies that sets are well defined. Observe that, if the theorem holds trivially due to Definition 9 and Lemma 7; this is because both nodes will trivially be informed about the same set, according to the first part of the definition of an informed node. Thus, we focus on the case where . The FIFOReceiveAll condition for nodes and is satisfied in the parallel execution for respectively by assumption. Let be the actual fault set. Due to the 3reach condition, there exists a node which is nonfaulty by definition of the reach set and is connected to through fully nonfaulty simple paths and respectively. Note that both nodes will only satisfy their FIFOReceiveAll conditions only if they receive messages of the form and , respectively, from through the existing nonfaulty paths respectively; this holds since due to Line 1, node (analogously node ) will wait until it receives from all paths entirely comprising of nodes in which include all nodes on . Thus, must have sent both , messages. Since these messages are FIFOflooded from and there are fully nonfaulty paths connecting with both , one of the two nodes will receive both , messages before satisfying the FIFOReceiveAll condition. Assume without loss of generality that this node is . We will then show that the theorem holds for .
Similar arguments ^{6}^{6}6This follows from the first paragraph of the proof of Lemma 7 for being any of the nodes . to the ones used in the proof of Lemma 7 imply that must have received a unique value for each from redundant paths in in order to satisfy its MaximalConsistency condition during the parallel execution for set . Similarly with previous arguments, by Theorem 5 and the strong connectivity of , there exists a simple path in , which propagates this value to . Since, by assumption, executes FilterandAverage during its parallel execution for , by the MaximalConsistency condition, will also receive the same unique value , for each such node , propagated by redundant path because is fully nonfaulty and entirely contained in . As argued previously,
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