Performative Federated Learning

Kun Jin, Tongxin Yin, Zhongzhu Chen, Zeyu Sun, Xueru Zhang, Yang Liu, Mingyan Liu

Keywords: robustness, federated learning, model-dependent data shifts, performative prediction, strategic classification and regression

TL;DR: We proposed the performative FL framework and the performative FedAvg (P-FedAvg) algorithm to address the model-dependent data distribution shift problems in FL. P-FedAvg converge at rate O(1/T) under the same assumptions in previous works.

Abstract:

We consider a federated learning (FL) system comprising multiple clients and a server, wherein the clients collaborate to learn a common decision model from their distributed data. Unlike the conventional FL framework, we consider the scenario where the clients' data distributions change with the deployed decision model. In this work, we propose a performative federated learning framework that formalizes model-dependent distribution shifts by leveraging the concept of distribution shift mappings in the performative prediction literature.
We introduce necessary and sufficient conditions for the existence of a unique performative stable solution and characterize its distance to the performative optimal solution. Under such conditions, we propose the performative FedAvg algorithm and show that it converges to the performative stable solution at a rate of O(1/T) under both full and partial participation schemes. In addition, we show how the clients' heterogeneity influences the convergence both theoretically and using numerical results.