5 papers
When Evidence Shapes Collaboration: Knowledge-Conditioned Topology Generation for Multi-Agent Systems
Yangxiao Jiang, Jiarun Fan, Mingcong Xu +6
Multi-Agent Systems (MAS) have recently moved from static workflows toward dynamically generated collaboration topologies. However, existing topology generation methods rely primar…
Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
Xiaojin Zhang, Mingcong Xu, Yiming Li +2
Federated learning (FL) offers a promising paradigm for collaborative model training while preserving data privacy. However, its susceptibility to gradient inversion attacks poses…
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning
Mingcong Xu, Xiaojin Zhang, Wei Chen +1
Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems. Howeve…
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
Xiaojin Zhang, Mingcong Xu, Wei Chen
In this paper, we first give an introduction to the theoretical basis of the privacy-utility equilibrium in federated learning based on Bayesian privacy definitions and total varia…