Federated learning with buffered asynchronous aggregation
Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …
Federated Learning with Buffered Asynchronous Aggregation
J Nguyen, K Malik, H Zhan, A Yousefpour… - arXiv e …, 2021 - ui.adsabs.harvard.edu
Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL-synchronized aggregation of client …
systems. In this work, we identify that synchronous FL-synchronized aggregation of client …
Federated Learning with Buffered Asynchronous Aggregation
J Nguyen, K Malik, H Zhan, A Yousefpour… - arXiv preprint arXiv …, 2021 - arxiv.org
Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL-synchronized aggregation of client …
systems. In this work, we identify that synchronous FL-synchronized aggregation of client …
[PDF][PDF] Federated Learning with Buffered Asynchronous Aggregation
J Nguyen, K Malik, H Zhan, A Yousefpour, M Rabbat… - fl-icml.github.io
Federated Learning (FL) trains a shared model across distributed devices while keeping the
training data on the devices. Most FL schemes are synchronous: they perform a …
training data on the devices. Most FL schemes are synchronous: they perform a …