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Found 4 papers, 2 papers with code
Confidential Federated Computations
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data.
Papaya: Practical, Private, and Scalable Federated Learning
Our work tackles the aforementioned issues, sketches of some of the system design challenges and their solutions, and touches upon principles that emerged from building a production FL system for millions of clients.
Federated Learning with Buffered Asynchronous Aggregation
On the other hand, asynchronous aggregation of client updates in FL (i. e., asynchronous FL) alleviates the scalability issue.
Towards Federated Learning at Scale: System Design
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
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