Routed Prototype Adapters for Federated Financial Return Prediction with Frozen LLMs

Abstract

Financial return prediction increasingly relies on both financial text and structured market covariates, but adapting large language models across financial institutions remains difficult because raw data cannot be centralized and clients often exhibit heterogeneous, non-stationary market signals. This paper studies data-local federated financial return prediction with a frozen LLM, aiming to share useful cross-client adaptation while preserving client-specific predictive behavior. We propose a federated routed-adapter framework in which the server maintains a pool of lightweight adapter prototypes, each client selects a personalized mixture of these prototypes through projected directional routing, and local residual adapters are learned on private client data around the selected mixture. The server then maps uploaded residual updates back to the shared prototype space through an exact least-norm decomposition for communication-efficient aggregation. The framework keeps raw financial data and client-private prediction heads local, while uploaded residuals remain model updates and should not be interpreted as a formal privacy guarantee without additional mechanisms such as secure aggregation or differential privacy. Across FNSPID, Qlib CSI300/CSI800, and Open FinLLM forecasting benchmarks, our method achieves the best overall performance, improving CSI300 RankIC from 0.082 to 0.087 over the strongest federated PEFT baseline and reducing FNSPID MAE from 0.00537 to 0.00482. These results suggest that compositional shared adaptation with local residual personalization is a practical direction for financial LLM deployment under data-local, communication-constrained, and heterogeneous federated settings.