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.

翻译：联邦学习（FL）作为一种变革性方法，能够在保护用户隐私的同时实现分布式机器学习，但仍面临通信效率低下和依赖中心化基础设施等挑战，导致延迟和成本增加。本文提出一种新颖的联邦学习方法，通过消除对边缘服务器的依赖，采用基于数据相似性、性能指标和地理邻近度的服务器辅助邻近性评估进行动态聚类构建，从而克服了这些限制。我们的集成方法通过混合去中心化聚合协议提升运行效率与可扩展性——该协议将本地模型训练与点对点权重交换相结合，并由动态选举的驱动节点执行中心化最终聚合，显著降低了全局通信开销。此外，该方法还包含去中心化驱动节点选择、用于减少网络流量的检查点机制，以及保障系统鲁棒性的健康状态验证机制。基于乳腺癌数据集的验证表明，该架构不仅实现了近十倍的通信开销降低，同时在保持高学习性能的前提下，显著减少了训练延迟与能耗，为联邦学习生态系统的未来发展提供了可扩展、高效且隐私保护的解决方案。