FedCod: An Efficient Communication Protocol for Cross-Silo Federated Learning with Coding

TL;DR

FedCod is a novel communication protocol for cross-silo federated learning that uses coding techniques to improve bandwidth utilization, reduce communication time, and adapt to network fluctuations without compromising training performance.

Contribution

FedCod introduces a coding-based communication protocol that enhances efficiency and robustness in cross-silo federated learning from a network perspective.

Findings

Reduces communication time by up to 62% in real-world tests.

Maintains training performance while optimizing communication traffic.

Adapts coding redundancy dynamically to network conditions.

Abstract

Federated Learning (FL) is an innovative distributed machine learning paradigm that enables multiple parties to collaboratively train a model without sharing their raw data, thereby preserving data privacy. Communication efficiency concerns arise in cross-silo FL, particularly due to the network heterogeneity and fluctuations associated with geo-distributed silos. Most existing solutions to these problems focus on algorithmic improvements that alter the FL algorithm but sacrificing the training performance. How to address these problems from a network perspective that is decoupled from the FL algorithm remains an open challenge. In this paper, we propose FedCod, a new application layer communication protocol designed for cross-silo FL. FedCod transparently utilizes a coding mechanism to enhance the efficient use of idle bandwidth through client-to-client communication, and dynamically adjusts coding redundancy to mitigate network bottlenecks and fluctuations, thereby improving the communication efficiency and accelerating the training process. In our real-world experiments, FedCod demonstrates a significant reduction in average communication time by up to 62% compared to the baseline, while maintaining FL training performance and optimizing inter-client communication traffic.