CRAFT: Conflict-Resolved Aggregation for Federated Training

2026-05-20

AI Summary

This work addresses the challenge of conflicting client updates in federated learning caused by heterogeneous data distributions. The authors propose a geometric projection-based constrained optimization framework for model aggregation, which formulates the global update as a closed-form solution that satisfies layer-wise conflict-free alignment constraints while remaining closest to a reference direction—enabling efficient, non-iterative aggregation. Theoretical analysis establishes guarantees for a common descent structure from the perspective of projection geometry. Experimental results demonstrate that the proposed method significantly improves global model accuracy on standard heterogeneous benchmarks and effectively reduces performance disparities among clients, outperforming current state-of-the-art approaches.

Abstract

The aggregation of conflicting client updates remains a fundamental bottleneck in federated learning (FL) under heterogeneous data distributions. Naive averaging can produce a global update that improves the global objective while conflicting with specific clients, causing degradation for those clients. In this work, we propose CRAFT (Conflict-Resolved Aggregation for Federated Training), a new aggregation framework that treats the global update as a geometric correction problem. We formulate aggregation as finding the update closest to a reference direction while satisfying conflict-free alignment constraints. We derive a closed-form expression for the constrained optimization problem, avoiding the computational overhead of iterative solvers. Furthermore, we use a layer-wise adaptation to address conflicts at varying feature granularities. We provide a theoretical analysis showing that CRAFT promotes a common-descent structure and mitigates conflicts through its projection geometry. Extensive experiments on heterogeneous benchmarks demonstrate that CRAFT improves the accuracy of the global model while reducing performance disparity across clients compared with state-of-the-art baselines. The source code for CRAFT is available at https://github.com/tum-pbs/CRAFT.

Problem

• federated learning
• heterogeneous data
• conflicting updates
• model aggregation
• client performance disparity

Innovation

• federated learning
• conflict resolution
• geometric aggregation
• heterogeneous data
• closed-form optimization