3 papers
cs.LG2026
Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View
Kun Zhao, Xu Chen
Federated learning enables collaborative model training across distributed clients without centralising their data, yet privacy remains a persistent concern because the shared mode…
cs.LG2026
All in One: Generative Modeling as Mean-Field Game Design
Kun Zhao, Xu Chen
Mean-field games (MFGs) offer a unifying lens on continuous-time generative modeling: a cost tuple recovering twelve prominent models---Continuous Normalizing Flows, OT-Flow, Score…
cs.LG2023
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Weijie Liu, Xiaoxi Zhang, Jingpu Duan +3
Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data. While prior works…