
ZIP-DL introduces a novel privacy-aware decentralized learning algorithm using correlated noise to enhance privacy without compromising model accuracy.


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low-cost-privacy-aware-decentralized-learning-zip-dl-algorithm


Low-Cost Privacy-Aware Decentralized Learning: ZIP-DL Algorithm


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SHATTER, a novel decentralized learning approach, enhances privacy by having nodes operate multiple virtual nodes that communicate random model chunks through a dynamically changing communication topology, preventing attackers from collecting full models or identifying the original node behind a given model chunk.


enhancing-privacy-in-decentralized-learning-through-virtual-nodes-and-randomized-model-sharing


Enhancing Privacy in Decentralized Learning through Virtual Nodes and Randomized Model Sharing
