SAFARI: Self-regulAted Clustered FederAted Learning in a HeteRogeneous EnvIronment

Federated Learning (FL) has emerged as a critical technology for enabling distributed machine learning while preserving data privacy. However, traditional FL faces significant challenges, including communication inefficiencies and reliance on central infrastructures, which lead to increased latency and costs. This paper introduces an innovative FL methodology that addresses these issues by eliminating the dependency on edge servers and employing a server-assisted Proximity Evaluation that dynamically clusters nodes based on data similarity, performance metrics, and geographical proximity. We propose a Hybrid Decentralized Aggregation Protocol that combines local model development with direct peer-to-peer model weight exchanges and centralized aggregation conducted by a dynamically selected driver, substantially reducing global communication overhead. Our system also incorporates Decentralized Driver Selection, Check-pointing to mitigate network congestion, and a Health Status Verification Mechanism to enhance system robustness. Evaluated across five different datasets, our approach demonstrates up to a tenfold reduction in communication requirements, significantly accelerates training speed, and improves energy efficiency, all while maintaining strong learning performance. This methodology offers a scalable, efficient, and secure architecture for the future of federated learning implementations.