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Giskard protocol enhances decentralized learning with robust aggregation · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Giskard, a new protocol designed to enhance security and efficiency in large-scale decentralized learning. Giskard addresses the challenge of simultaneously maintaining data confidentiality and defending against Byzantine (malicious or faulty) participants. The protocol organizes participants into a tree of committees, enabling a more scalable approach to aggregation compared to existing methods that require all-to-all communication or heavily burden a small subset of nodes. Experiments with up to one million participants demonstrate Giskard's ability to reduce communication complexity while maintaining model utility even with a significant proportion of Byzantine parties. AI

IMPACT Improves scalability and security for decentralized AI model training.

RANK_REASON The cluster contains a research paper detailing a new protocol for decentralized learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ousmane Touat, C\'esar Sabater, Mohamed Maouche, Sonia Ben Mokhtar ·

    Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

    arXiv:2606.19129v1 Announce Type: cross Abstract: Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and…

  2. arXiv cs.LG TIER_1 English(EN) · Sonia Ben Mokhtar ·

    Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

    Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a …

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