In Federated Learning (FL), with parameter aggregated by a central node, the communication overhead is a substantial concern. To circumvent this limitation and alleviate the single point of failure within the FL framework, recent studies have introduced Decentralized Federated Learning (DFL) as a viable alternative. Considering the device heterogeneity, and energy cost associated with parameter aggregation, in this paper, the problem on how to efficiently leverage the limited resources available to enhance the model performance is investigated. Specifically, we formulate a problem that minimizes the loss function of DFL while considering energy and latency constraints. The proposed solution involves optimizing the number of local training rounds across diverse devices with varying resource budgets. To make this problem tractable, we first analyze the convergence of DFL with edge devices with different rounds of local training. The derived convergence bound reveals the impact of the rounds of local training on the model performance. Then, based on the derived bound, the closed-form solutions of rounds of local training in different devices are obtained. Meanwhile, since the solutions require the energy cost of aggregation as low as possible, we modify different graph-based aggregation schemes to solve this energy consumption minimization problem, which can be applied to different communication scenarios. Finally, a DFL framework which jointly considers the optimized rounds of local training and the energy-saving aggregation scheme is proposed. Simulation results show that, the proposed algorithm achieves a better performance than the conventional schemes with fixed rounds of local training, and consumes less energy than other traditional aggregation schemes.

翻译：在联邦学习（FL）中，参数经由中心节点聚合，通信开销是一个重要问题。为规避这一限制并缓解FL框架中的单点故障，近期研究引入去中心化联邦学习（DFL）作为可行替代方案。考虑到设备异构性及参数聚合所伴随的能量成本，本文研究了如何有效利用有限资源以提升模型性能的问题。具体而言，我们构建了一个同时考虑能量与延迟约束的DFL损失函数最小化问题。所提出的解决方案涉及优化不同资源预算设备上的本地训练轮次。为使该问题易于处理，我们首先分析了具有不同本地训练轮次的边缘设备在DFL中的收敛性。推导出的收敛界揭示了本地训练轮次对模型性能的影响。随后，基于该收敛界，得到了不同设备本地训练轮次的闭式解。同时，由于这些解要求聚合能量成本尽可能低，我们修改了多种基于图的聚合方案以解决能量消耗最小化问题，这些方案可适用于不同通信场景。最后，提出了一种联合考虑优化本地训练轮次与节能聚合方案的DFL框架。仿真结果表明，所提算法相比固定本地训练轮次的传统方案实现了更优性能，且能耗低于其他传统聚合方案。