FedBWO: Enhancing Communication Efficiency in Federated Learning

TL;DR

FedBWO is a novel federated learning approach that reduces communication costs by transmitting performance scores instead of full model weights, significantly improving accuracy and efficiency on resource-limited devices.

Contribution

This paper introduces FedBWO, a new method that uses the BWO algorithm to enhance local updates and minimize data transmission in federated learning.

Findings

FedBWO improves global model accuracy by 21% over FedAvg.

It reduces communication costs significantly.

Experimental results confirm enhanced performance and efficiency.

Abstract

Federated Learning (FL) is a distributed Machine Learning (ML) setup, where a shared model is collaboratively trained by various clients using their local datasets while keeping the data private. Considering resource-constrained devices, FL clients often suffer from restricted transmission capacity. Aiming to enhance the system performance, the communication between clients and server needs to be diminished. Current FL strategies transmit a tremendous amount of data (model weights) within the FL process, which needs a high communication bandwidth. Considering resource constraints, increasing the number of clients and, consequently, the amount of data (model weights) can lead to a bottleneck. In this paper, we introduce the Federated Black Widow Optimization (FedBWO) technique to decrease the amount of transmitted data by transmitting only a performance score rather than the local model weights from clients. FedBWO employs the BWO algorithm to improve local model updates. The conducted experiments prove that FedBWO remarkably improves the performance of the global model and the communication efficiency of the overall system. According to the experimental outcomes, FedBWO enhances the global model accuracy by an average of 21% over FedAvg, and 12% over FedGWO. Furthermore, FedBWO dramatically decreases the communication cost compared to other methods.