Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning

Riccardo Salami, Pietro Buzzega, Matteo Mosconi, Jacopo Bonato, Luigi Sabetta, Simone Calderara

Keywords: model merging, federated continual learning, federated learning, continual learning

TL;DR: We introduce LoRM, an alternating optimization strategy for merging low-rank adapted models, achieving state-of-the-art performance in Federated Class-Incremental Learning by effectively integrating multiple models into a unified system.

Abstract:

Model merging has emerged as a crucial technique in Deep Learning, enabling the integration of multiple models into a unified system while preserving performance and scalability. In this respect, the compositional properties of low-rank adaptation techniques (e.g., LoRA) have proven beneficial, as simple averaging LoRA modules yields a single model that mostly integrates the capabilities of all individual modules. Building on LoRA, we take a step further by imposing that the merged model matches the responses of all learned modules. Solving this ob-
jective in closed form yields an indeterminate system with A and B as unknown variables, indicating the existence of infinitely many closed-form solutions. To address this challenge, we introduce LoRM, an alternating optimization strategy that trains one LoRA matrix at a time. This allows solving for each unknown variable individually, thus finding a unique solution. We apply our proposed methodology to Federated Class-Incremental Learning (FCIL), ensuring alignment of model responses both between clients and across tasks. Our method demonstrates state-of-the-art performance across a range of FCIL scenarios. The code to reproduce our experiments is available at github.com/aimagelab/fed-mammoth.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 7931