Privacy Preserving Gossip Learning
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 previously learned samples while protecting private data. This gives each agent three roles: (i) a learner that updates the model parameters, (ii) a teacher whose sample is learned at the current iteration, and (iii) a protected agent whose sample has already been learned. We build on Tuning without Forgetting (TwF) method to preserve previously learned mappings and show that TwF provides an indistinguishability guarantee for the learner whenever the set of protected a...
| Authors | Erkan Bayram, Mohamed-Ali Belabbas, Tamer Başar |
|---|---|
| Primary category | cs.LG |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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