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.

在分布式学习中，为了解决参数聚合带来的通信开销和单点故障问题，本论文研究了如何有效利用有限资源以提高模型性能。我们通过优化本地训练轮次的数量和节能聚合方案，提出了一个解决方案，该方案在不同设备上实现了更好的性能表现，并消耗比传统聚合方案更少的能量。

受能量和延迟约束的自适应分散式联邦学习

Adaptive Decentralized Federated Learning in Energy and Latency  Constrained Wireless Networks

Data-driven industrial health prognostics require rich training data to
develop accurate and reliable predictive models. However, stringent data
privacy laws and the abundance of edge industrial data necessitate
decentralized data utilization. Thus, the industrial health prognostics field
is well suited to significantly benefit from federated learning (FL), a
decentralized and privacy-preserving learning technique. However, FL-based
health prognostics tasks have hardly been investigated due to the complexities
of meaningfully aggregating model parameters trained from heterogeneous data to
form a high performing federated model. Specifically, data heterogeneity among
edge devices, stemming from dissimilar degradation mechanisms and unequal
dataset sizes, poses a critical statistical challenge for developing accurate
federated models. We propose a pioneering FL-based health prognostic model with
a feature similarity-matched parameter aggregation algorithm to
discriminatingly learn from heterogeneous edge data. The algorithm searches
across the heterogeneous locally trained models and matches neurons with
probabilistically similar feature extraction functions first, before
selectively averaging them to form the federated model parameters. As the
algorithm only averages similar neurons, as opposed to conventional naive
averaging of coordinate-wise neurons, the distinct feature extractors of local
models are carried over with less dilution to the resultant federated model.
Using both cyclic degradation data of Li-ion batteries and non-cyclic data of
turbofan engines, we demonstrate that the proposed method yields accuracy
improvements as high as 44.5\% and 39.3\% for state-of-health estimation and
remaining useful life estimation, respectively.

本文提出了一种 FL-based 健康预测模型和一种特征相似度匹配的参数聚合算法，以从异构数据中区分性地学习，利用循环衰减数据和涡轮鼓风机的非循环数据，为状态健康估计和剩余寿命估计带来高达 44.5％和 39.3％的精度提高。