Constant Term Shrinkage for Federated Learning

Federated Learning (FL) enables collaborative model training without centralizing private data, but its performance often degrades under data heterogeneity because local updates become biased toward client-specific data characteristics. In this paper, we propose Constant Term Shrinkage for Federated Learning (FedCTS)}, a simple server-side aggregation method designed to mitigate such update drift. FedCTS decomposes each client update into a constant term and a residual term. We show that the constant term is strongly affected by data heterogeneity. FedCTS therefore evaluates the reliability of the constant term and adaptively shrinks the unreliable constant term toward zero while leaving the residual term unchanged. The shrinkage strength is obtained in closed form from clients' update statistics, without requiring an additional tuning hyperparameter. Experiments across multiple datasets and FL algorithms demonstrate that FedCTS improves robustness under heterogeneous data with negligible additional computation, no extra communication overhead, and strong compatibility with existing FL techniques.