ERIS: Enhancing Privacy and Communication Efficiency in Serverless Federated Learning

• URL: http://arxiv.org/abs/2602.08617v1
• Date: Mon, 09 Feb 2026 13:05:41 GMT
• Title: ERIS: Enhancing Privacy and Communication Efficiency in Serverless Federated Learning
• Authors: Dario Fenoglio, Pasquale Polverino, Jacopo Quizi, Martin Gjoreski, Marc Langheinrich,
• Abstract summary: ERIS is a serverless FL framework that balances privacy and accuracy while eliminating the server bottleneck and distributing the communication load.<n>We theoretically prove that ERIS converges at the same rate as FedAvg under standard assumptions, and (ii) bounds mutual information leakage inversely with the number of aggregators.
• Score: 6.486831630436399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scaling federated learning (FL) to billion-parameter models introduces critical trade-offs between communication efficiency, model accuracy, and privacy guarantees. Existing solutions often tackle these challenges in isolation, sacrificing accuracy or relying on costly cryptographic tools. We propose ERIS, a serverless FL framework that balances privacy and accuracy while eliminating the server bottleneck and distributing the communication load. ERIS combines a model partitioning strategy, distributing aggregation across multiple client-side aggregators, with a distributed shifted gradient compression mechanism. We theoretically prove that ERIS (i) converges at the same rate as FedAvg under standard assumptions, and (ii) bounds mutual information leakage inversely with the number of aggregators, enabling strong privacy guarantees with no accuracy degradation. Experiments across image and text tasks, including large language models, confirm that ERIS achieves FedAvg-level accuracy while substantially reducing communication cost and improving robustness to membership inference and reconstruction attacks, without relying on heavy cryptography or noise injection.

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