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[2602.17614] Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
Summary
This paper presents KD-UFSL, a method to enhance privacy in federated split learning by minimizing data leakage through intermediate representations while maintaining model utility.
Why It Matters
As federated learning becomes more prevalent in handling sensitive data, ensuring privacy without sacrificing performance is crucial. This research addresses the vulnerabilities in intermediate data representations, providing a solution that balances privacy and utility, which is essential for large-scale applications.
Key Takeaways
- KD-UFSL employs k-anonymity and differential privacy to protect client data.
- The method significantly reduces the risk of data leakage through intermediate representations.
- Experiments show a trade-off between privacy enhancement and model utility preservation.
- The approach is suitable for big data applications requiring privacy and performance.
- Demonstrates the effectiveness of privacy-enhancing techniques in federated learning.
Computer Science > Machine Learning arXiv:2602.17614 (cs) [Submitted on 19 Feb 2026] Title:Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning Authors:Obaidullah Zaland, Sajib Mistry, Monowar Bhuyan View a PDF of the paper titled Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning, by Obaidullah Zaland and 2 other authors 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. We first demonstrate that an adversary can access private client data from intermedi...
