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Federated learning (FL) has emerged as a distributed machine learning (ML)technique to train models without sharing users' private data. In this paper,we propose a decentralized FL scheme that is called \underline{f}ederated\underline{l}earning \underline{e}mpowered \underline{o}verlapped\underline{c}lustering for \underline{d}ecentralized aggregation (FL-EOCD). Theintroduced FL-EOCD leverages device-to-device (D2D) communications andoverlapped clustering to enable decentralized aggregation, where a cluster isdefined as a coverage zone of a typical device. The devices located on theoverlapped clusters are called bridge devices (BDs). In the proposed FL-EOCDscheme, a clustering topology is envisioned where clusters are connectedthrough BDs, so as the aggregated models of each cluster is disseminated to theother clusters in a decentralized manner without the need for a globalaggregator or an additional hop of transmission. Unlike the star-based FL, theproposed FL-EOCD scheme involves a large number of local devices by reusing theRRBs in different non-adjacent clusters. To evaluate our proposed FL-EOCDscheme as opposed to baseline FL schemes, we consider minimizing the overallenergy-consumption of devices while maintaining the convergence rate of FLsubject to its time constraint. To this end, a joint optimization problem,considering scheduling the local devices/BDs to the CHs and computationfrequency allocation, is formulated, where an iterative solution to this jointproblem is devised. Extensive simulations are conducted to verify theeffectiveness of the proposed FL-EOCD algorithm over FL conventional schemes interms of energy consumption, latency, and convergence rate.

Decentralized Aggregation for Energy-Efficient Federated Learning via Overlapped Clustering and D2D Communications