Nazemi, N;
Tavallaie, O;
Chen, S;
Mandalari, AM;
Thilakarathna, K;
Holz, R;
Haddadi, H;
(2025)
ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Networks for FLaaS.
In:
Proceedings of the IEEE International Conference on Web Services Icws.
(pp. pp. 225-231).
IEEE: Helsinki, Finland.
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Abstract
Federated Learning (FL) enables privacy-preserving machine learning by allowing clients to collaboratively train models without sharing raw data. Federated Learning as a Service (FLaaS) extends this approach to cloud infrastructures. However, conventional secure aggregation protocols, such as Google's SecAgg and SecAgg+, introduce high computation and communication overheads, particularly in large-scale FLaaS deployments where client dropout rates are limited. To address these challenges, we propose ACCESS-FL, a lightweight, secure aggregation method designed for honest-but-curious FLaaS scenarios with stable network conditions. ACCESS-FL eliminates double masking, Shamir's Secret Sharing, and excessive encryption/decryption by creating shared secrets only between two peers per client, which reduces computation and communication complexity to constant $O(1)$ and makes the algorithm independent of network size and comparable to standard FL. ACCESS-FL preserves privacy against inversion attacks and maintains model accuracy equivalent to the FL, SecAgg, and SecAgg+ protocols, proving that reducing overhead does not compromise learning performance and achieves communication and computation costs comparable to standard FL. Experimental evaluations on benchmark datasets (MNIST, FMNIST, and CIFAR-10) demonstrate lower overhead, making ACCESS-FL practical for service-based stable FLaaS applications such as healthcare analytics.
| Type: | Proceedings paper |
|---|---|
| Title: | ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Networks for FLaaS |
| Event: | 2025 IEEE International Conference on Web Services (ICWS) |
| Dates: | 7 Jul 2025 - 12 Jul 2025 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICWS67624.2025.00036 |
| Publisher version: | https://doi.org/10.1109/icws67624.2025.00036 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Data privacy, Costs, Protocols, Federated learning, Web services, Computational modeling, Medical services, Computational efficiency, Cryptography, Standards |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216566 |
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