SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment

TL;DR

This paper introduces a novel federated learning approach that eliminates reliance on edge servers, reduces communication costs, and improves scalability and efficiency through dynamic clustering and decentralized protocols.

Contribution

It proposes a server-assisted clustering and hybrid decentralized aggregation method that significantly reduces communication overhead and enhances scalability in federated learning.

Findings

Achieved nearly tenfold reduction in communication overhead

Reduced training latency and energy consumption

Maintained high learning performance

Abstract

Federated Learning (FL) has emerged as a transformative approach for enabling distributed machine learning while preserving user privacy, yet it faces challenges like communication inefficiencies and reliance on centralized infrastructures, leading to increased latency and costs. This paper presents a novel FL methodology that overcomes these limitations by eliminating the dependency on edge servers, employing a server-assisted Proximity Evaluation for dynamic cluster formation based on data similarity, performance indices, and geographical proximity. Our integrated approach enhances operational efficiency and scalability through a Hybrid Decentralized Aggregation Protocol, which merges local model training with peer-to-peer weight exchange and a centralized final aggregation managed by a dynamically elected driver node, significantly curtailing global communication overhead. Additionally, the methodology includes Decentralized Driver Selection, Check-pointing to reduce network traffic, and a Health Status Verification Mechanism for system robustness. Validated using the breast cancer dataset, our architecture not only demonstrates a nearly tenfold reduction in communication overhead but also shows remarkable improvements in reducing training latency and energy consumption while maintaining high learning performance, offering a scalable, efficient, and privacy-preserving solution for the future of federated learning ecosystems.