Collaborative and Efficient Personalization with Mixtures of Adaptors

Abstract

Heterogenous data is prevalent in real-world federated learning. We propose a parameter-efficient framework, Federated Low-Rank Adaptive Learning (FLoRAL), that allows clients to personalize in groups by mixing between low-rank adaptors, where the mixtures are client-specific. FLoRAL is a model parameterization that casts personalized federated learning as a multi-task learning problem, with weight sharing as an implicit regularizer. It is memory-efficient, as the personalized parameters (i.e., base model + adaptors) are all federated. Our results show that FLoRAL can generalize better than a mixture of full models when data are scarce. It can also consistently personalize better than models with a locally tuned adaptor per client. This demonstrates the benefits of "federated personalization" and its robustness against overfitting. We derive the convergence rates and show theoretically that FLoRAL can lead to better variance reduction of the base model's gradients.