https://crypto-ppml.github.io/2022/

https://slideslive.com/38968419/renyi-differential-privacy-of-the-subsampled-shuffle-model-in-distributed-learning?ref=search-presentations

https://slideslive.com/38953178/shuffled-model-of-differential-privacy-in-federated-learning?ref=speaker-71232

https://slideslive.com/38940957/shuffled-model-of-federated-learning-privacy-accuracy-and-communication-tradeoffs?ref=speaker-54993

During a round of decentralized learning for updating of a global machine learning (ML) model, remote processor (s) of a remote system may transmit, to a population of computing devices, primary weights for a primary version of the global ML model, and cause each of the computing devices to generate a corresponding update for the primary version of the global ML model. Further, the remote processor (s) may cause the primary version of the global ML model to be updated based on the corresponding updates that are received during the round of decentralized learning. However, the remote processor (s) may receive other corresponding updates subsequent to the round of decentralized learning. Accordingly, various techniques described herein (eg, FARe-DUST, FeAST on MSG, and/or other techniques) enable the other corresponding updates to be utilized in achieving a final version of the global ML model.

https://www.sciencehub.ucla.edu/2021-amazon-fellows/