  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.
