Towards Node Liability in Federated Learning: Computational Cost and Network Overhead

by   Francesco Malandrino, et al.

Many machine learning (ML) techniques suffer from the drawback that their output (e.g., a classification decision) is not clearly and intuitively connected to their input (e.g., an image). To cope with this issue, several explainable ML techniques have been proposed to, e.g., identify which pixels of an input image had the strongest influence on its classification. However, in distributed scenarios, it is often more important to connect decisions with the information used for the model training and the nodes supplying such information. To this end, in this paper we focus on federated learning and present a new methodology, named node liability in federated learning (NL-FL), which permits to identify the source of the training information that most contributed to a given decision. After discussing NL-FL's cost in terms of extra computation, storage, and network latency, we demonstrate its usefulness in an edge-based scenario. We find that NL-FL is able to swiftly identify misbehaving nodes and to exclude them from the training process, thereby improving learning accuracy.


Federated Learning: Opportunities and Challenges

Federated Learning (FL) is a concept first introduced by Google in 2016,...

OpenFL: An open-source framework for Federated Learning

Federated learning (FL) is a computational paradigm that enables organiz...

Federated Learning at the Network Edge: When Not All Nodes are Created Equal

Under the federated learning paradigm, a set of nodes can cooperatively ...

Industrial Federated Learning – Requirements and System Design

Federated Learning (FL) is a very promising approach for improving decen...

Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point

We propose cooperative edge-assisted dynamic federated learning (CE-FL)....

Oort: Informed Participant Selection for Scalable Federated Learning

Federated Learning (FL) is an emerging direction in distributed machine ...

Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems

Next-generation autonomous and networked industrial systems (i.e., robot...