FedRand: Enhancing Privacy in Federated Learning with Randomized LoRA
  Subparameter Updates
Authors: name: Sangwoo Park, name: Seanie Lee, name: Byungjoo Kim, name: Sung Ju Hwang

Federated Learning (FL) is a widely used framework for training models in a
decentralized manner, ensuring that the central server does not have direct
access to data from local clients. However, this approach may still fail to
fully preserve data privacy, as models from local clients are exposed to the
central server during the aggregation process. This issue becomes even more
critical when training vision-language models (VLMs) with FL, as VLMs can
easily memorize training data instances, making them vulnerable to membership
inference attacks (MIAs). To address this challenge, we propose the FedRand
framework, which avoids disclosing the full set of client parameters. In this
framework, each client randomly selects subparameters of Low-Rank Adaptation
(LoRA) from the server and keeps the remaining counterparts of the LoRA weights
as private parameters. After training both parameters on the client's private
dataset, only the non-private client parameters are sent back to the server for
aggregation. This approach mitigates the risk of exposing client-side VLM
parameters, thereby enhancing data privacy. We empirically validate that
FedRand improves robustness against MIAs compared to relevant baselines while
achieving accuracy comparable to methods that communicate full LoRA parameters
across several benchmark datasets.