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New decentralized learning algorithm enhances privacy with gossip protocol

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel decentralized learning algorithm designed to preserve privacy. This method allows agents to learn from individual private samples while sequentially updating a shared model. The algorithm builds upon the Tuning without Forgetting (TwF) technique to maintain previously learned mappings and offers an indistinguishability guarantee for the learner under specific conditions. For the teacher agent, a minimax optimal control problem is formulated to balance privacy and performance, while protected agents' contributions are aggregated using a private push-sum gossip protocol. The approach is proven to converge geometrically for both the decentralized gossip and distributed projection aspects. AI

IMPACT Introduces a new method for secure decentralized learning, potentially enabling more private data collaboration in AI model training.

RANK_REASON The cluster contains a research paper detailing a novel algorithm for decentralized privacy-preserving learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New decentralized learning algorithm enhances privacy with gossip protocol

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The cluster contains a research paper detailing a novel algorithm for decentralized privacy-preserving learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Erkan Bayram, Mohamed-Ali Belabbas, Tamer Ba\c{s}ar ·

    Privacy Preserving Gossip Learning

    arXiv:2609.14778v1 Announce Type: new Abstract: We propose a decentralized privacy-preserving learning algorithm in which each agent holds a single private sample and a shared model. Samples are learned sequentially, and each update must preserve the endpoint mappings at previous…

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