Federated Learning with Reduced Information Leakage and Computation

no code implementations • 10 Oct 2023 • Tongxin Yin, Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu

Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data.

Federated Learning Privacy Preserving

Fair Classifiers that Abstain without Harm

no code implementations • 9 Oct 2023 • Tongxin Yin, Jean-François Ton, Ruocheng Guo, Yuanshun Yao, Mingyan Liu, Yang Liu

To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner.

Decision Making Fairness

Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts

no code implementations • 8 May 2023 • Kun Jin, Tongxin Yin, Zhongzhu Chen, Zeyu Sun, Xueru Zhang, Yang Liu, Mingyan Liu

We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distributed data.

Federated Learning