[2512.03336] Single-Round Scalable Analytic Federated Learning

Computer Science > Machine Learning arXiv:2512.03336 (cs) [Submitted on 3 Dec 2025 (v1), last revised 29 Mar 2026 (this version, v2)] Title:Single-Round Scalable Analytic Federated Learning Authors:Alan T. L. Bacellar, Mustafa Munir, Felipe M. G. França, Priscila M. V. Lima, Radu Marculescu, Lizy K. John View a PDF of the paper titled Single-Round Scalable Analytic Federated Learning, by Alan T. L. Bacellar and 5 other authors View PDF HTML (experimental) Abstract:Federated Learning (FL) is plagued by two key challenges: high communication overhead and performance collapse on heterogeneous (non-IID) data. Analytic FL (AFL) provides a single-round, data distribution invariant solution, but is limited to linear models. Subsequent non-linear approaches, like DeepAFL, regain accuracy but sacrifice the single-round benefit. In this work, we break this trade-off. We propose SAFLe, a framework that achieves scalable non-linear expressivity by introducing a structured head of bucketed features and sparse, grouped embeddings. We prove this non-linear architecture is mathematically equivalent to a high-dimensional linear regression. This key equivalence allows SAFLe to be solved with AFL's single-shot, invariant aggregation law. Empirically, SAFLe establishes a new state-of-the-art for analytic FL, significantly outperforming both linear AFL and multi-round DeepAFL in accuracy across all benchmarks, demonstrating a highly efficient and scalable solution for federated vision. Comment...