arXiv cs.AI· Quan Minh Nguyen, Hoang M. Ngo, Bhakti Chowkwale, Yasaman Keshtkarjahromi, My T. Thai·· 11 小时前AI 评分26
FedFiOT：面向单轮与少轮联邦学习的 Fisher 加权最优传输模型合并框架
FedFiOT: Fisher-Weighted Optimal Transport for Model Merging in One-Shot and Few-Round Federated Learning
AI 导读
FedFiOT 用 Fisher 信息量化参数重要性，联合优化神经元对应关系与全局模型参数，解决单轮及少轮联邦学习中客户端模型神经元错位与参数重要性差异导致的直接平均失效问题。理论上其交替最小化过程单调降低逐层合并目标，并给出量化客户端异构与非均匀 Fisher 影响的平稳性界。多个数据集实验显示，该方法在单轮与少轮联邦学习设置下均稳定优于对比基线。
正文 · 原文
Abstract:One-shot and few-round federated learning (FL) reduce communication burden by constructing a global model within one or a few communication rounds, but often achieve lower performance than conventional multi-round FL. Without frequent synchronization, client models may drift apart, causing neurons that learn similar features to occupy different positions across clients. At the same time, local data heterogeneity can cause corresponding parameters to differ in their importance to each client's predictions. Together, these challenges cause direct averaging to combine mismatched neurons and assign equal weight to parameters despite differences in their importance. We propose FedFiOT, a Fisher-weighted optimal transport framework for one-shot and few-round FL that leverages Fisher information to quantify parameter importance and guide neuron alignment and model aggregation. FedFiOT jointly optimizes neuron correspondences and global model parameters through alternating minimization of a shared Fisher-weighted objective. Theoretically, we show that FedFiOT's optimization procedure monotonically decreases the layer-wise merging objective and establish a stationarity bound that quantifies the effects of client heterogeneity and nonuniform Fisher this http URL across multiple datasets demonstrate consistent improvements over competing baselines in both one-shot and few-round federated learning settings.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06783 [cs.LG] |
| (or arXiv:2611.06783v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06783 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Quan Nguyen [view email]
[v1]
Wed, 4 Nov 2026 16:56:10 UTC (535 KB)
来源：arXiv cs.AI · arxiv.org