ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning

When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources. In addition to typical limitations such as data, computation, and communication costs, access to the models is also often limited. This paper endeavors to solve both the challenges of limited resources and personalization. PFL that uses Zeroth-Order Optimization for Personalized Federated Learning. PFL avoids direct interference with the foundation models and instead learns to adapt its inputs through zeroth-order optimization. In addition, we employ simple yet effective linear projections to remap its predictions for personalization. To reduce the computation costs and enhance personalization, we propose input surgery to incorporate an auto-encoder with low-dimensional and client-specific embeddings. PFL to analyze its convergence. Extensive empirical experiments on computer vision and natural language processing tasks using popular foundation models demonstrate its effectiveness for FL on black-box foundation models. In recent years, the growing emphasis on data privacy and security has led to the emergence of federated learning (FL) (Warnat-Herresthal et al., 2021; Chen & Chao, 2022; Chen et al., 2023b; Castiglia et al., 2023; Rodríguez-Barroso et al., 2023; Kuang et al., 2023). FL enables collaborative learning while safeguarding data privacy and security across distributed clients (Yang et al., 2019). However, FL faces two key challenges: limited resources and distribution shifts (Figure 1 (a, b)). The rise of large foundation models (Bommasani et al., 2021) has amplified these challenges. The computational demands and communication costs associated with such models hinder the deployment of existing FL approaches (Figure 1a).