Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication

02/01/2019 ∙ by Anastasia Koloskova, et al. ∙ EPFL 0

We consider decentralized stochastic optimization with the objective function (e.g. data samples for machine learning task) being distributed over n machines that can only communicate to their neighbors on a fixed communication graph. To reduce the communication bottleneck, the nodes compress (e.g. quantize or sparsify) their model updates. We cover both unbiased and biased compression operators with quality denoted by ω≤ 1 (ω=1 meaning no compression). We (i) propose a novel gossip-based stochastic gradient descent algorithm, CHOCO-SGD, that converges at rate O(1/(nT) + 1/(T δ^2 ω)^2) for strongly convex objectives, where T denotes the number of iterations and δ the eigengap of the connectivity matrix. Despite compression quality and network connectivity affecting the higher order terms, the first term in the rate, O(1/(nT)), is the same as for the centralized baseline with exact communication. We (ii) present a novel gossip algorithm, CHOCO-GOSSIP, for the average consensus problem that converges in time O(1/(δ^2ω) (1/ϵ)) for accuracy ϵ > 0. This is (up to our knowledge) the first gossip algorithm that supports arbitrary compressed messages for ω > 0 and still exhibits linear convergence. We (iii) show in experiments that both of our algorithms do outperform the respective state-of-the-art baselines and CHOCO-SGD can reduce communication by at least two orders of magnitudes.



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1 Introduction

Decentralized machine learning methods are becoming core aspects of many important applications, both in view of scalability to larger datasets and systems, but also from the perspective of data locality, ownership and privacy. In this work we address the general data-parallel setting where the data is distributed across different compute devices, and consider decentralized optimization methods that do not rely on a central coordinator (e.g. parameter server) but instead only require on-device computation and local communication with neighboring devices. This covers for instance the classic setting of training machine learning models in large data-centers, but also emerging applications were the computations are executed directly on the consumer devices, which keep their part of the data private at all times.111Note the optimization process itself (as for instance the computed result) might leak information about the data of other nodes. We do not focus on quantifying notions of privacy in this work.

Formally, we consider optimization problems distributed across devices or nodes of the form


where for are the objectives defined by the local data available on each node. We also allow each local objective

to have stochastic optimization (or sum) structure, covering the important case of empirical risk minimization in distributed machine learning and deep learning applications.

Decentralized Communication.

We model the network topology as a graph with edges if and only if nodes and are connected by a communication link, meaning that these nodes directly can exchange messages (for instance computed model updates). The decentralized setting is motivated by centralized topologies (corresponding to a star graph) often not being possible, and otherwise often posing a significant bottleneck on the central node in terms of communication latency, bandwidth and fault tolerance. Decentralized topologies avoid these bottlenecks and thereby offer hugely improved potential in scalability. For example, while the master node in the centralized setting receives (and sends) in each round messages from all workers, in total222For better connected topologies sometimes more efficient all-reduce and broadcast implementations are available., in decentralized topologies the maximal degree of the network is often constant (e.g. ring or torus) or a slowly growing function in (e.g. scale-free networks).

Decentralized Optimization.

For the case of deterministic (full-gradient) optimization, recent seminal theoretical advances show that the network topology only affects higher-order terms of the convergence rate of decentralized optimization algorithms on convex problems (Scaman et al., 2017, 2018). We prove the first analogue result for the important case of decentralized stochastic gradient descent (SGD), proving convergence at rate (ignoring for now higher order terms) on strongly convex functions where denotes the number of iterations.

This result is significant since stochastic methods are highly preferred for their efficiency over deterministic gradient methods in machine learning applications. Our algorithm, Choco-SGD, is as efficient in terms of iterations as centralized mini-batch SGD (and consequently also achieves a speedup of factor compared to the serial setting on a single node) but avoids the communication bottleneck that centralized algorithms suffer from.

Communication Compression.

In distributed training, model updates (or gradient vectors) have to be exchanged between the worker nodes. To reduce the amount of data that has to be send, gradient

compression has become a popular strategy. For instance by quantization (Alistarh et al., 2017; Wen et al., 2017; Lin et al., 2018) or sparsification (Wangni et al., 2018; Stich et al., 2018).

These ideas have recently been introduced also to the decentralized setting by Tang et al. (2018a). However, their analysis only covers unbiased compression operators with very (unreasonably) high accuracy constraints. Here we propose the first method that supports arbitrary low accuracy and even biased compression operators, such as in (Alistarh et al., 2018; Lin et al., 2018; Stich et al., 2018).


Our contributions can be summarized as follows:

  • We show that the proposed Choco-SGD converges at rate , where denotes the number of iterations, the number of workers, the eigengap of the gossip (connectivity) matrix and the compression quality factor ( meaning no compression). We show that the decentralized method achieves the same speedup as centralized mini-batch SGD when the number  of workers grows. The network topology and the compression only mildly affect the convergence rate. This is verified experimentally on the ring topology and by reducing the communication by a factor of 100 ().

  • We present the first provably-converging gossip algorithm with communication compression, for the distributed average consensus problem. Our algorithm, Choco-Gossip, converges linearly at rate for accuracy , and allows arbitrary communication compression operators (including biased and unbiased ones). In contrast, previous work required very high-precision quantization and could only show convergence towards a neighborhood of the optimal solution.

  • Choco-SGD significantly outperforms state-of-the-art methods for decentralized optimization with gradient compression, such as ECD-SGD and DCD-SGD introduced in (Tang et al., 2018a), in all our experiments.

2 Related Work

Stochastic gradient descent (SGD) (Robbins & Monro, 1951; Bottou, 2010) and variants thereof are the standard algorithms for machine learning problems of the form (1), though it is an inherit serial algorithm that does not take the distributed setting into account. Mini-batch SGD (Dekel et al., 2012) is the natural parallelization of SGD for (1) in the centralized setting, i.e. when a master node collects the updates from all worker nodes, and serves a baseline here.

Decentralized Optimization.

The study of decentralized optimization algorithms can be tracked back at least to the 1980s (Tsitsiklis, 1984). Decentralized algorithms are sometimes referred to as gossip algorithms (Kempe et al., 2003; Xiao & Boyd, 2004; Boyd et al., 2006) as the information is not broadcasted by a central entity, but spreads—similar as gossip—along the edges specified by the communication graph. The most popular algorithms are based on (sub)gradient descent (Nedić & Ozdaglar, 2009; Johansson et al., 2010), alternating direction method of multipliers (ADMM) (Wei & Ozdaglar, 2012; Iutzeler et al., 2013) or dual averaging (Duchi et al., 2012; Nedić et al., 2015). He et al. (2018) address the more specific problem class of generalized linear models.
For the deterministic (non-stochastic) convex version of (1) a recent line of work developed optimal algorithms based on acceleration  (Jakovetić et al., 2014; Scaman et al., 2017, 2018; Uribe et al., 2018). Rates for the stochastic setting are derived in (Shamir & Srebro, 2014; Rabbat, 2015), under the assumption that the distributions on all nodes are equal. This is a strong restriction which prohibits most distributed machine learning applications. Our algorithm Choco-SGD avoids any such assumption. Also, (Rabbat, 2015) requires multiple communication rounds per stochastic gradient computation and so is not suited for sparse communication, as the required number of communication rounds would increase proportionally to the sparsity. Lan et al. (2018) applied gradient sliding techniques allowing to skip some of the communication rounds.
Lian et al. (2017); Tang et al. (2018b, a); Assran et al. (2018) consider the non-convex setting with Tang et al. (2018a) also applying gradient quantization techniques to reduce the communication cost. However, their algorithms require very high precision quantization, a constraint we can overcome here.

Gradient Compression.

Instead of transmitting a full dimensional (gradient) vector , methods with gradient compression transmit a compressed vector instead, where is a (random) operator chosen such that can be more efficiently represented, for instance by using limited bit representation (quantization) or enforcing sparsity. A class of very common quantization operators is based on random dithering (Goodall, 1951; Roberts, 1962) that is in addition also unbiased, , , see (Alistarh et al., 2017; Wen et al., 2017; Zhang et al., 2017). Much sparser vectors can be obtained by random sparsification techniques that randomly mask the input vectors and only preserve a constant number of coordinates (Wangni et al., 2018; Konecny & Richtárik, 2018; Stich et al., 2018). Techniques that do not directly quantize gradients, but instead maintain additional states are known to perform better in theory and practice (Seide et al., 2014; Lin et al., 2018; Stich et al., 2018), an approach that we pick up here. Our analysis also covers deterministic and biased compression operators, such as in (Alistarh et al., 2018; Stich et al., 2018). We will not further distinguish between sparsification and quantization approaches, and refer to both of them as compression operators in the following.

Distributed Average Consensus.

In the decentralized setting, the average consensus problem consists in finding the average vector of local vectors (see (2) below for a formal definition). The problem is an important sub-routine of many decentralized algorithms. It is well known that gossip-type algorithms converge linearly for average consensus  (Kempe et al., 2003; Xiao & Boyd, 2004; Olfati-Saber & Murray, 2004; Boyd et al., 2006). However, for consensus algorithms with compressed communication it has been remarked that the standard gossip algorithm does not converge to the correct solution (Xiao et al., 2005). The proposed schemes in (Carli et al., 2007; Nedić et al., 2008; Aysal et al., 2008; Carli et al., 2010b; Yuan et al., 2012) do only converge to a neighborhood (whose size depends on the compression accuracy) of the solution.
In order to converge, adaptive schemes (with varying compression accuracy) have been proposed (Carli et al., 2010a; Fang & Li, 2010; Li et al., 2011; Thanou et al., 2013). However, these approaches fall back to full (uncompressed) communication to reach high accuracy. In contrast, our method converges linearly to the true solution, even for arbitrary compressed communication, without requiring adaptive accuracy. We are not aware of a method in the literature with similar guarantees.

3 Average Consensus with Communication Compression

In this section we present Choco-Gossip, a novel gossip algorithm for distributed average consensus with compressed communication. As mentioned, the average consensus problem is an important special case of type (1), and formalized as


for vectors distributed on nodes (consider in (1)). Our proposed algorithm will later serve as a crucial primitive in our optimization algorithm for the general optimization problem (1), but is of independent interest for any average consensus problem with communication constraints.

In Sections 3.13.3 below we first review existing schemes that we later consider as baselines for the numerical comparison. The novel algorithm follows in Section 3.4.

3.1 Gossip algorithms

The classic decentralized algorithms for the average consensus problem are gossip type algorithms (see e.g. (Xiao & Boyd, 2004)) that generate sequences on every node by iterations of the form


Here denotes a stepsize parameter, averaging weights and denotes a vector that is sent from node to node in iteration . Note that no communication is required if . If we assume symmetry, , the weights naturally define the communication graph with edges if and self-loops for . The convergence rate of scheme (3) crucially depends on the connectivity matrix of the network defined as , also called the interaction or gossip matrix.

Definition 1 (Gossip matrix).

We assume that is a symmetric () doubly stochastic (,

) matrix with eigenvalues

and spectral gap


It will also be convenient to define

and (5)

Table 1 gives a few values of the spectral gap for commonly used network topologies (with uniform averaging between the nodes). It is well known that simple matrices with do exist for every connected graph.

graph/topology node degree
ring 2
2d-torus 4
fully connected
Table 1: Spectral gap for some important network topologies on nodes (see e.g. (Aldous & Fill, 2002, p. 169)) for uniformly averaging , i.e. for .

3.2 Gossip with Exact Communication

For a fixed gossip matrix , the classical algorithm analyzed in (Xiao & Boyd, 2004) corresponds to the choice


in (3), with (E-G) standing for exact gossip. This scheme can also conveniently be written in matrix notation as


for iterates .

Theorem 1.

Let and be the spectral gap of . Then the iterates of (E-G) converge linearly to the average with the rate

For this corresponds to the classic result in e.g. (Xiao & Boyd, 2004), here we slightly extend the analysis for arbitrary stepsizes. The short proof shows the elegance of the matrix notation (that we will later also adapt for the proofs that will follow).

Proof for ..

Let . Then for the theorem follows from the observation

Here on the second line we used the crucial identity , i.e. the algorithm preserves the average over all iterations. This can be seen from (6):

by Definition 1. The proof for arbitrary follows the same lines and is given in the appendix. ∎

3.3 Gossip with Quantized Communication

In every round of scheme (E-G) a full dimensional vector is exchanged between two neighboring nodes for every link on the communication graph (node sends to all its neighbors , ). A natural way to reduce the communication is to compress before sending it, denoted as , for a (potentially random) compression . Informally, we can think of as either a sparsification operator (that enforces sparsity of ) or a quantization operator that reduces the number of bits required to represent . For instance random rounding to less precise floating point numbers or to integers.

Aysal et al. (2008) propose the quantized gossip (Q1-G),


in scheme (3), i.e. to apply the compression operator directly on the message that is send out from node to node . However, this algorithm does not preserve the average of the iterates over the iterations, for , and as a consequence does not converge to the optimal solution of (2) (though in practice often to a close neighborhood).

An alternative proposal by Carli et al. (2007) alleviates this drawback. The scheme


preserves the average of the iterates over the iterations. However, the scheme also fails to converge for arbitrary precision. If , the noise introduced by the compression, , does not vanish for . As a consequence, the iterates oscillate around when compression error becomes larger than the suboptimality .

Both these schemes have been theoretically studied in (Carli et al., 2010b) under assumption of unbiasendness, i.e. assuming for all (and we will later also adopt this theoretically understood setting in our experiments).

3.4 Proposed Method for Compressed Communication

We propose the novel compressed gossip scheme Choco-Gossip that supports a much larger class of compression operators, beyond unbiased quantization as for the schemes above. The algorithm can be summarized as


for a stepsize depending on the specific compression operator (this will be detailed below). Here denote additional variables that are stored333A closer look reveals that actually only 2 additional vectors have to be stored per node (refer to Appendix E). by all neighbors of node , , as well as on node itself.

We will show in Theorem 2 below that this scheme (i) preserves the averages of the iterates , over the iterations . Moreover, (ii) the noise introduced by the compression operator vanishes as . Precisely, we will show that for for every . Consequently, the argument for in (Choco-G) goes to zero, and the noise introduced by can be controlled.

The proposed scheme is summarized in Algorithm 1. Every worker stores and updates its own local variable as well as the variables for all neighbors (including itself) .

0:  : Initial values on each node , stepsize , communication graph and mixing matrix , initialize
1:  for  in  do in parallel for all workers
3:     for neighbors (including do
4:        Send and receive
6:     end for
8:  end for
Algorithm 1 Choco-Gossip

Algorithm 1 seems to require each machine to store vectors. This is not necessary and the algorithm could be re-written in a way that every node stores only three vectors: , and . For simplicity, we omit this technical modification here and refer to Appendix E for the exact form of the memory-efficient algorithm.

3.5 Convergence Analysis for Choco-Gossip

We analyze Algorithm 1 under the following general quality notion for the compression operator .

Assumption 1 (Compression operator).

We assume that the compression operator satisfies


for a parameter . Here denotes the expectation over the internal randomness of operator .

Example operators that satisfy (7) include

  • sparsification: Randomly selecting out of coordinates (), or the coordinates with highest magnitude values () give (Stich et al., 2018, Lemma A.1).

  • randomized gossip: Setting

    with probability

    and otherwise, gives .

  • rescaled unbiased estimators

    : suppose , and , then satisfies (7) with .

  • random quantization: For precision (levels) , and the quantization operator

    for random variable

    satisfies (7) with (Alistarh et al., 2017, Lemma 3.1).

Theorem 2.

Choco-Gossip (Algorithm 1) converges linearly for average consensus:

when using the stepsize , where  is the compression factor as in Assumption 1, and .

For the proof we refer to the appendix, where we used matrix notation to simplify derivations. For the exact communication case we recover the rate from Theorem 1 for stepsize up to constant factors (which seems to be a small artifact of our proof technique). The theorem shows convergence for arbitrary , showing the superiority of scheme (Choco-G) over (Q1-G) and (Q2-G).

4 Decentralized Stochastic Optimization

In this section we leverage our proposed average consensus Algorithm 1 to achieve consensus among the compute nodes in a decentralized optimization setting with communication restrictions.

In the decentralized optimization setting (1), not only does every node have a different local objective , but we also allow each to have stochastic optimization (or sum) structure, that is


for a loss function

and distributions which can be different on every node. Our framework therefore covers both stochastic optimization (e.g. when all are identical) and empirical risk minimization (as in machine learning and deep learning applications) when the ’s are discrete with disjoint support.

4.1 Proposed Scheme for Decentralized Optimization

Our proposed method Choco-SGD—Communication-Compressed Decentralized SGD—is stated in Algorithm 2.

0:  : Initial values on each node , consensus stepsize , SGD stepsizes , communication graph and mixing matrix , initialize
1:  for  in  do in parallel for all workers
2:     Sample , compute gradient
5:     for neighbors (including do
6:        Send and receive
8:     end for
10:  end for
Algorithm 2 Choco-SGD

The algorithm consists of four parts. The stochastic gradient step in line 3, application of the compression operator in step 4, and the (Choco-G) local communication in lines 5–8 followed by the final iterate update in line 9.

Remark 3.

As a special case without any communication compression, and for consensus stepsize as in exact gossip (E-G), Choco-SGD (Algorithm 2) recovers the following standard variant of decentralized SGD with gossip (similar e.g. to (Sirb & Ye, 2016; Lian et al., 2017)), stated for illustration in Algorithm 3.

1:  for  in  do in parallel for all workers
2:     Sample , compute gradient
4:     Send to neighbors
6:  end for
Algorithm 3 Plain Decentralized SGD

4.2 Convergence Analysis for Choco-SGD

Assumption 2.

We assume that each function for is -smooth and

-strongly convex and that the variance on each worker is bounded

where denotes the expectation over . It will be also convenient to denote

For the (standard) definitions of smoothness and strong convexity we refer to Appendix A.1. These assumptions could be relaxed to only hold for , the set of iterates of Algorithm 2.

Theorem 4.

Under Assumption 2, Algorithm 2 with SGD stepsizes for parameter for condition number and consensus stepsize chosen as in Theorem 2, converges with the rate

where for an averaged iterate with weights , and . As reminder, denotes the eigengap of , and the compression ratio.

For the proof we refer to the appendix. When and are sufficiently large, the second two terms become negligible compared to —and we recover the convergence rate of of mini-batch SGD in the centralized setting and with exact communication. This is because topology (parameter ) and compression (parameter only affect the higher-order terms in the rate. We also see that we obtain in this setting a speed up compared to the serial implementation of SGD on only one worker.










Figure 1: Ring topology (left) and Torus topology (right).

5 Experiments

In this section we first compare Choco-Gossip to the gossip baselines from Section 5.2 and then compare the Choco-SGD to state of the art decentralized stochastic optimization schemes (that also support compressed communication) in Section 5.3.

5.1 Shared Experimental Setup

For our experiments we always report the number of iterations of the respective scheme, as well as the number of transmitted bits. These quantities are independent of systems architectures and network bandwidth.


In the experiments we rely on the (Sonnenburg et al., 2008) and (Lewis et al., 2004) datasets (cf. Table 4).

Compression operators.

We use the (), () and compression operators as described in Section 3.5, where we choose to be of all coordinates and , only requiring , respectively bits to represent a coordinate.

Note that in contrast to Choco-Gossip, the earlier schemes (Q1-G) and (Q2-G) were both analyzed in (Carli et al., 2010b) for unbiased compression operators. In order to reflect this theoretical understood setting we use the rescaled operators () and in combination with those schemes.

5.2 Average Consensus

Figure 2: Average consensus on the ring topology with nodes, coordinates and compression
Figure 3: Average consensus on the ring topology with nodes, coordinates and () and () compression
dataset density epsilon rcv1
Table 2: Size and density of the datasets.
experiment Choco, 1 Choco, () 0.011 Choco, () 0.046
Table 3: Tuned stepsizes for averaging in Figs. 3– 3.
Plain 0.1 - 1 1 -
Choco, 0.34 1 1 0.078
Choco, () 0.1 0.01 1 1 0.016
Choco, () 0.1 0.04 1 1 0.04
DCD, () - -
DCD, 0.01 - -
ECD, () - -
ECD, () - -
Table 4: Parameters for the SGD learning rate and consensus learning used in the experiments in Figs. 56. Parameters where tuned separately for each algorithm. Tuning details can be found in Appendix F. The ECD and DCD stepsizes are small because the algorithms were observed to diverge for larger choices.

We compare the performance of the gossip schemes (E-G) (exact communication), (Q1-G), (Q2-G) (both with unbiased compression), and our scheme (Choco-G) in Figure 3 for the compression scheme and in Figure 3 for the random () compression scheme. In addition, we also depict the performance of Choco-Gossip with biased () compression. We use ring topology with uniformly averaging mixing matrix W as in Figure 1, left. The stepsizes that were used for Choco-Gossip are listed in the Table 4. We consider here the consensus problem (2) with data on the -machine was chosen to be the -th vector in the dataset. We depict the errors .

The proposed scheme (Choco-G) with 8 bit quantization converges with the same rate as (E-G) that uses exact communications (Fig. 3, left), while it requires much less data to be transmitted (Fig. 3, right). The schemes (Q1-G) and  (Q2-G) can do not converge and reach only accuracies of . The scheme (Q1-G) even starts to diverge, because the quantization error becomes larger than the optimization error.

With sparsified communication (), i.e. transmitting only of all the coordinates, the scheme (Q1-G) quickly zeros out all the coordinates, and (Q2-G) diverges because quantization error is too large already from the first step (Fig. 3). Choco-Gossip proves to be more robust and converges. The observed rate matches with the theoretical findings, as we expect the scheme with factor compression to be slower than (E-G) without compression. In terms of total data transmitted, both schemes converge at the same speed (Fig. 3, right). We also see that () sparsification can give additional gains and comes out as the most data-efficient method in these experiments.

5.3 Decentralized SGD

Figure 4: Performance of Algorithm 3 on ring, torus and fully connected topologies for nodes. Here we consider the sorted setting, whilst the performance for randomly shuffled data is depicted in the Appendix G.
Figure 5: Comparison of Algorithm 3 (plain), ECD-SGD, DCD-SGD and Choco-SGD with () sparsification (in addition () for Choco-SGD), for (top) and (bottom) in terms of iterations (left) and communication cost (right), .
Figure 6: Comparison of Algorithm 3 (plain), ECD-SGD, DCD-SGD and Choco-SGD with () quantization, for (top) and (bottom) in terms of iterations (left) and communication cost (right), on nodes on a ring topology.

We asses the performance of Choco-SGD

on logistic regression, defined as

, where and are the data samples and denotes the number of samples in the dataset. We distribute the data samples evenly among the workers and consider two settings: (i) randomly shuffled, where datapoints are randomly assigned to workers, and the more difficult (ii) sorted

setting, where each worker only gets data samples just from one class (with the possible exception of one worker that gets two labels assigned). Moreover, we try to make the setting as difficult as possible, meaning that e.g. on the ring topology the machines with the same label form two connected clusters. We repeat each experiment three times and depict the mean curve and the area corresponding to one standard deviation. We plot suboptimality, i.e.

(obtained by optimizer from scikit-learn (Pedregosa et al., 2011)) versus number of iterations and the number of transmitted bits between workers, which is proportional to the actual running time if communication is a bottleneck.


As baselines we consider Alg. 3 with exact communication (denoted as ‘plain’) and the communication efficient state-of-the-art optimization schemes DCD-SGD and ECD-SGD recently proposed in (Tang et al., 2018a) (for unbiased quantization operators) and compare them to Choco-SGD. We use decaying stepsize where the parameters are individually tuned for each algorithm and compression scheme, with values given in Table 4.

Impact of Topology.

In Figure 4 we depict the performance of the baseline Algorithm 3 with exact communication on different topologies (ring, torus and fully-connected; Fig. 1) with uniformly averaging mixing matrix . Note that Algorithm 3 for fully-connected graph corresponds to mini-batch SGD. Increasing the number of workers from to and shows the mild effect of the network topology on the convergence. We observe that the sorted setting is more difficult than the randomly shuffled setting (see Fig. 7 in the Appendix G), where the convergence behavior remains almost unaffected. In the following we focus on the hardest case, i.e. the ring topology.

Comparison to Baselines.

In Figures 5 and 6 depict the performance of these algorithms on the ring topology with nodes for sorted data of the and datasets. Choco-SGD performs almost as good as the exact Algorithm 3 in all situations, but using less communication with () sparsification (Fig. 5, right) and approximately less communication for quantization. The () variant performs slightly better than () sparsification.

Choco-SGD consistently outperforms DCD-SGD in all settings. We also observed that DCD-SGD starts to perform better for larger number of levels in the in the quantification operator (increasing communication cost). This is consistent with the reporting in (Tang et al., 2018a) that assumed high precision quantization. As a surprise to us, ECD-SGD, which was proposed in (Tang et al., 2018a) a the preferred alternative over DCD-SGD for less precise quantization operators, always performs worse than DCD-SGD, and often diverges.

Figures for randomly shuffled data and be found in the Appendix G. In that case Choco-SGD performs exactly as well as the exact Algorithm 3 in all situations.


The experiments verify our theoretical findings: Choco-Gossip is the first linearly convergent gossip algorithm with quantized communication and Choco-SGD consistently outperforms the baselines for decentralized optimization, reaching almost the same performance as the exact algorithm without communication restrictions while significantly reducing communication cost. In view of the striking popularity of SGD as opposed to full-gradient methods for deep-learning, the application of Choco-SGD to decentralized deep learning—an instance of problem (1)— is a promising direction.


We acknowledge funding from SNSF grant 200021_175796, as well as a Google Focused Research Award.


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Appendix A Basic Identities and Inequalities

a.1 Smooth and Strongly Convex Functions

Definition 2.

A differentiable function is -strongly convex for parameter if

Definition 3.

A differentiable function is -strongly convex for parameter if

Remark 5.

If is -smooth with minimizer s.t , then


a.2 Vector and Matrix Inequalities

Remark 6.

For ,

Remark 7.

For arbitrary set of vectors ,

Remark 8.

For given two vectors

Remark 9.

For given two vectors


This inequality also holds for the sum of two matrices in Frobenius norm.

a.3 Implications of the bounded gradient and bounded variance assumption

Remark 10.

If are convex functions with ,

Remark 11 (Mini-batch variance).

If for functions , defined in (8) , then

where .


This follows from

for . Expectation of scalar product is equal to zero because is independent of since . ∎

Appendix B Consensus in Matrix notation

In the proofs in the next section we will use the matrix notation, as already introduced in the main text. We define


Then using matrix notation we can rewrite Algorithm 1 as

0:  : , , .
1:  Initialize: