FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots

TL;DR

This paper introduces FedAR, a federated learning model tailored for resource-constrained mobile robots, which monitors client activity, assigns trust scores, and employs asynchronous aggregation to enhance learning efficiency and reliability.

Contribution

It proposes a novel federated learning framework that accounts for resource limitations and trustworthiness of mobile robot clients, improving distributed learning in IoT environments.

Findings

Trust score effectively identifies unreliable clients

Asynchronous aggregation reduces straggler effects

Resource-aware client selection improves learning speed

Abstract

Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices are considered the primary data source for a distributed network. Due to a revolutionary breakthrough in internet availability and continuous improvement of the IoT devices capabilities, it is desirable to store data locally and perform computation at the edge, as opposed to share all local information with a centralized computation agent. A recently proposed Machine Learning (ML) algorithm called Federated Learning (FL) paves the path towards preserving data privacy, performing distributed learning, and reducing communication overhead in large-scale machine learning (ML) problems. This paper proposes an FL model by monitoring client activities and leveraging available local computing resources, particularly for resource-constrained IoT devices (e.g., mobile robots), to accelerate the learning process. We assign a trust score to each FL client, which is updated based on the client's activities. We consider a distributed mobile robot as an FL client with resource limitations either in memory, bandwidth, processor, or battery life. We consider such mobile robots as FL clients to understand their resource-constrained behavior in a real-world setting. We consider an FL client to be untrustworthy if the client infuses incorrect models or repeatedly gives slow responses during the FL process. After disregarding the ineffective and unreliable client, we perform local training on the selected FL clients. To further reduce the straggler issue, we enable an asynchronous FL mechanism by performing aggregation on the FL server without waiting for a long period to receive a particular client's response.