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We propose a new asynchronous FL scheme that combines adjusting client training. The scheme determines the weight of aggregation based on the staleness of ...
Federated Learning is a widely adopted method to train neural networks over distributed data. One main limitation is the performance degradation that occurs ...
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Jul 25, 2024 · This paper introduces federated adaptive asynchronous optimization, named FADAS, a novel method that incorporates asynchronous updates into adaptive federated ...
Jun 4, 2024 · We propose FedAST, a buffered asynchronous federated simultaneous training algorithm that overcomes bottlenecks from slow models and adaptively allocates ...
Asynchronous federated learning (AFL) is a distributed ma- chine learning technique that allows multiple devices to col- laboratively train deep learning ...
We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in ...
Jun 1, 2024 · Federated Learning (FL) enables edge devices or clients to collaboratively train machine learning (ML) models without sharing their private data ...
Nov 9, 2023 · Federated learning is a mechanism for model training in distributed systems, aiming to protect data privacy while achieving collective ...
Motivated by these chal- lenges, we propose and analyze FedBuff, a novel asynchronous federated optimization framework us- ing buffered asynchronous aggregation ...
Oct 10, 2023 · In this paper, we introduce an innovative asynchronous hierarchical FL approach based on bandwidth allocation and client scheduling.