• Computer Science > Machine Learning [Submitted on 19 Feb 2026] Title:Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning View PDF HTML (experimental)Abstract:Big data scenarios, where massive, heterogeneous datasets are distributed across clients, demand scalable, privacy-preserving learning methods. • Federated learning (FL) enables decentralized training of machine learning (ML) models across clients without data centralization. • Decentralized training, however, introduces a computational burden on client devices. • U-shaped federated split learning (UFSL) offloads a fraction of the client computation to the server while keeping both data and labels on the clients’ side. • However, the intermediate representations (i.e., smashed data) shared by clients with the server are prone to exposing clients’ private data. • To reduce exposure of client data through intermediate data representations, this work proposes k-anonymous differentially private UFSL (KD-UFSL), which leverages privacy-enhancing techniques such as microaggregation and differential privacy to minimize data leakage from the smashed data transferred to the server.
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- Computer Science > Machine Learning [Submitted on 19 Feb 2026] Title:Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning View PDF HTML (experimental)Abstract:Big data scenarios, where massive, heterogeneous datasets are distributed across clients, demand scalable, privacy-preserving learning methods. Federated learning (FL) enables decentralized training of machine learning (ML) models across clients without data centralization. Decentralized training, however, introduces a computational burden on client devices. U-shaped federated split learning (UFSL) off
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