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arXiv Machine Learning By Ekkehard Schnoor, Malik Tiomoko, Alexander Jung

A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization

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The paper presents a large‑dimensional analysis of Generalized Total Variation Minimization (GTVMin) for least‑squares federated learning. It shows that Lipschitz observations of the global GTVMin solution concentrate in high dimensions regardless of network structure, and derives exact analytic expressions for performance under both i.i.d. and non‑i.i.d. Gaussian mixture models. These results quantify how regularization balances local adaptation against collaboration, and are validated by experiments on synthetic and real‑world data.

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