  With the recent success of large language models, particularly foundation
models with generalization abilities, applying foundation models for
recommendations becomes a new paradigm to improve existing recommendation
systems. It becomes a new open challenge to enable the foundation model to
capture user preference changes in a timely manner with reasonable
communication and computation costs while preserving privacy. This paper
proposes a novel federated adaptation mechanism to enhance the foundation
model-based recommendation system in a privacy-preserving manner. Specifically,
each client will learn a lightweight personalized adapter using its private
data. The adapter then collaborates with pre-trained foundation models to
provide recommendation service efficiently with fine-grained manners.
Importantly, users' private behavioral data remains secure as it is not shared
with the server. This data localization-based privacy preservation is embodied
via the federated learning framework. The model can ensure that shared
knowledge is incorporated into all adapters while simultaneously preserving
each user's personal preferences. Experimental results on four benchmark
datasets demonstrate our method's superior performance. Implementation code is
available to ease reproducibility.
