FERA: Uncertainty-Aware Federated Reasoning for Large Language Models

TL;DR

FERA introduces an uncertainty-aware federated reasoning framework that enhances large language models' multi-step reasoning by iteratively synthesizing client-generated reasoning traces without centralized data sharing.

Contribution

The paper proposes a novel training-free federated reasoning method with uncertainty estimates, enabling effective multi-client collaboration and improved reasoning accuracy.

Findings

FERA outperforms federated training and training-free baselines on reasoning benchmarks.

Uncertainty-aware weighting accelerates convergence of the reasoning process.

The iterative protocol guarantees convergence with theoretical support.

Abstract

Large language models (LLMs) exhibit strong reasoning capabilities when guided by high-quality demonstrations, yet such data is often distributed across organizations that cannot centralize it due to regulatory, proprietary, or institutional constraints. We study federated reasoning, where a server improves multi-step reasoning by coordinating with heterogeneous clients holding private demonstrations, without centralized training or raw data sharing. The key challenge is that client reliability is query-dependent, while the server cannot inspect client data to determine which contributions are trustworthy. To address this, we propose Uncertainty-Aware Federated Reasoning (FERA), a training-free framework based on iterative server-client co-refinement. Across communication rounds, clients generate reasoning traces with lightweight uncertainty estimates, and the server synthesizes them into improved reasoning that is redistributed as context for the next round, progressively improving both server outputs and client-side reasoning. Within each round, Uncertainty-Aware Self-Critique Aggregation (UA-SCA) resolves conflicts among heterogeneous client traces through query-dependent trust weighting and structured cross-client verification. Rather than simply discarding low-quality traces, UA-SCA revises flawed reasoning steps to recover useful information. We provide theoretical guarantees showing that the proposed iterative protocol converges and that uncertainty-aware weighting accelerates convergence. Experiments on multiple reasoning benchmarks show that FERA consistently outperforms both federated training and training-free baselines, achieving progressively higher accuracy across rounds while maintaining communication and computational efficiency.