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Federated learning advances boost privacy and cut communication costs

By PulseAugur Editorial · [5 sources] ·

Researchers have developed new methods to enhance privacy and efficiency in federated learning. One approach focuses on reducing communication costs by using top-K gradient sparsification, which transmits only essential gradient information while maintaining model accuracy. Another framework, DDP-SA, combines local differential privacy with secret sharing to ensure that no single server can access individual client data, offering stronger privacy guarantees than existing methods. These advancements aim to make federated learning more scalable and secure, particularly for large models and decentralized systems. AI

IMPACT These methods aim to make federated learning more practical and secure for large-scale AI model training.

RANK_REASON Multiple academic papers proposing new techniques for federated learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

Federated learning advances boost privacy and cut communication costs

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Research
Multiple academic papers proposing new techniques for federated learning.
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5 independent sources
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paper, safety, infra
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116 days old
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COVERAGE [5]

  1. arXiv cs.LG TIER_1 English(EN) · Hengxuan Tang, Jinbao Zhu, Xiaohu Tang ·

    Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning

    arXiv:2606.10780v1 Announce Type: cross Abstract: Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with the gradient dimension. This becomes prohibitive for large models and even more p…

  2. arXiv cs.LG TIER_1 English(EN) · Xiaohu Tang ·

    Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning

    Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with the gradient dimension. This becomes prohibitive for large models and even more pronounced in decentralized federated learning with…

  3. arXiv cs.LG TIER_1 English(EN) · Wenjing Wei, Farid Nait-Abdesselam, Alla Jammine ·

    Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation

    arXiv:2604.07125v2 Announce Type: replace-cross Abstract: This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy (LDP) and full-threshold additive secret sharing (ASS) for secure aggre…

  4. arXiv cs.LG TIER_1 English(EN) · Lanxin Yi, Jinbao Zhu, Kai Wan, Xiaohu Tang ·

    The Capacity of Information-Theoretic Secure Aggregation in Federated Learning

    arXiv:2606.07277v1 Announce Type: cross Abstract: Secure aggregation allows a server to aggregate users' local updates while preserving update privacy. Existing information-theoretic problems typically assume that correlated random keys are provided by a trusted third party (TTP)…

  5. arXiv cs.LG TIER_1 English(EN) · Xiaohu Tang ·

    The Capacity of Information-Theoretic Secure Aggregation in Federated Learning

    Secure aggregation allows a server to aggregate users' local updates while preserving update privacy. Existing information-theoretic problems typically assume that correlated random keys are provided by a trusted third party (TTP) or generated via prescribed groupwise structures,…

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