Allocate Before You Perturb: Adaptive Shared Release Spaces for Client-Level Differentially Private Federated Learning

Federated learning enables collaboration without centralizing raw data. Client-level differential privacy (DP) protects each client's entire dataset. However, at a fixed per-coordinate noise scale, expected isotropic Gaussian noise energy grows linearly with perturbation dimension, degrading utility in high dimensions. Independent client supports can expand during aggregation, weakening control over this dimension. We propose FedEASE, an entropy adaptive framework for shared sparse release that allocates before perturbation. From a fixed public partition and historical DP statistics, FedEASE samples a fixed number of blocks shared across clients before current updates form. This controls aggregation dimension. Entropy regularization balances historical scores and continued block selection. Inverse marginal correction adjusts sparse updates before clipping and compensates for unequal observation frequencies in the history. Noise aware feedback updates history using only released DP aggregates, without querying unprivatized client statistics. We establish client-level privacy, support coverage, and nonconvex convergence guarantees. Extensive experiments on four image classification datasets and IMDB demonstrate FedEASE's accuracy advantages over the compared DP baselines. These advantages are more pronounced under stronger noise.