Personalized federated learning adapts speed and accuracy to agents
Best of Both Worlds in Federated LSA: Speedup When Possible, Personalization Always
Machine Learning
Summary
This paper looks at a way for many different machines or agents to learn personalized tasks together, even if their tasks are slightly different. The challenge is to create one method that can both speed up learning when tasks are similar and still work well when tasks vary a lot. The authors introduce a simple new method called PF-LSA that mixes what each agent learns locally with the average learning across all agents. This method automatically adjusts without knowing how different the tasks are and guarantees quick and personalized learning for each agent.
What this means in practice
- •For mobile app developers: Enable apps to train personalized models collaboratively on user devices with faster learning when user behaviors are similar.
- •For telecommunications engineers: Coordinate distributed network devices to quickly adapt settings while maintaining device-specific customization in varying network conditions.
Authors
Safwan Labbi, Paul Mangold, Eric Moulines
Abstract
We study personalized federated linear stochastic approximation (LSA), a framework which notably encompass personalized temporal difference learning. In this setting, heterogeneous agents collaborate to solve distinct linear fixed-point equations, each corresponding to an agent-specific learning problem. A central open question in personalized learning is whether a single method can adapt to an unknown level of heterogeneity by converging to each agent's personalized solution in all regimes while achieving a linear speedup in the number of agents when their learning problems are sufficiently similar. We answer this question affirmatively by introducing PF-LSA, a minimalist algorithm that mixes each agent's local stochastic update with the average update across agents, at no additional computational cost relative to standard federated methods. We prove that PF-LSA, achieves best-of-both-worlds guarantees without any prior knowledge on the level of heterogeneity. Our analysis is based on a sharp decomposition of the error into consensus and disagreement components. The consensus error decays rapidly, whereas the disagreement error decays more slowly but becomes negligible in low-heterogeneity regimes.