arXiv cs.AI· Dario Fenoglio, Daniil Kirilenko, Martin Gjoreski, Marc Langheinrich·· 3 天前AI 评分18
FedCRF：用条件随机场结构化概率推断实现联邦学习聚合
Federated Learning with Energy-Based Structured Probabilistic Inference
AI 导读
研究者提出 FedCRF，将联邦学习的服务器聚合建模为基于条件随机场的结构化概率推断，把客户端表示为图节点并定义一元与成对能量势，从而联合推断客户端可靠性与相似关系。推断出的可靠性和亲和度可统一支撑全局聚合、拜占庭鲁棒性、聚类联邦学习和个性化联邦学习。在 CIFAR-10、FEMNIST 和 Fed-ISIC2019 上，单一超参数配置下其表现与专用方法相当或更优。
正文 · 原文
Abstract:Federated Learning (FL) enables multiple clients to collaboratively train machine learning models without sharing their local data. However, heterogeneity in client data and the presence of unreliable or adversarial updates make effective server aggregation challenging, motivating methods that adapt client contributions according to their reliability and relationships. In this work, we formulate server aggregation in FL as structured probabilistic inference using conditional random fields. We represent clients as nodes in a graph and define unary and pairwise energy potentials. Unary potentials capture individual client properties, while pairwise potentials encode similarities between clients. This formulation treats individual client evidence and pairwise relationships as coupled evidence in a joint inference problem, allowing client reliabilities to be inferred collectively and the resulting estimates to guide aggregation. Where most FL methods target a single challenge, FedCRF's inferred reliabilities and affinities provide one common representation for global aggregation, Byzantine robustness, clustered FL, and personalized FL. With a single set of hyperparameters, it is competitive with or outperforms specialized methods on CIFAR-10, FEMNIST, and Fed-ISIC2019 across diverse forms of client heterogeneity and unreliability.
| Comments: | Revised version (23 pages) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.30161 [cs.LG] |
| (or arXiv:2606.30161v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.30161 arXiv-issued DOI via DataCite |
Submission history
From: Dario Fenoglio [view email]
[v1]
Mon, 29 Jun 2026 11:38:46 UTC (165 KB)
[v2]
Fri, 9 Oct 2026 19:57:18 UTC (194 KB)
来源：arXiv cs.AI · arxiv.org