PersA-FL: Personalized Asynchronous Federated Learning
This work addresses scalability issues in federated learning for heterogeneous data, but it is incremental as it builds on existing personalization frameworks like MAML and ME.
The paper tackles the problem of personalized federated learning under asynchronous updates, aiming to improve scalability by removing synchronous communication assumptions, and demonstrates convergence to a first-order stationary point for smooth non-convex functions with experiments on heterogeneous datasets.
We study the personalized federated learning problem under asynchronous updates. In this problem, each client seeks to obtain a personalized model that simultaneously outperforms local and global models. We consider two optimization-based frameworks for personalization: (i) Model-Agnostic Meta-Learning (MAML) and (ii) Moreau Envelope (ME). MAML involves learning a joint model adapted for each client through fine-tuning, whereas ME requires a bi-level optimization problem with implicit gradients to enforce personalization via regularized losses. We focus on improving the scalability of personalized federated learning by removing the synchronous communication assumption. Moreover, we extend the studied function class by removing boundedness assumptions on the gradient norm. Our main technical contribution is a unified proof for asynchronous federated learning with bounded staleness that we apply to MAML and ME personalization frameworks. For the smooth and non-convex functions class, we show the convergence of our method to a first-order stationary point. We illustrate the performance of our method and its tolerance to staleness through experiments for classification tasks over heterogeneous datasets.