Device Scheduling and Update Aggregation Policies for Asynchronous
Federated Learning
- URL: http://arxiv.org/abs/2107.11415v1
- Date: Fri, 23 Jul 2021 18:57:08 GMT
- Title: Device Scheduling and Update Aggregation Policies for Asynchronous
Federated Learning
- Authors: Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
- Abstract summary: Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework.
We propose an asynchronous FL framework with periodic aggregation to eliminate the straggler issue in FL systems.
- Score: 72.78668894576515
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated Learning (FL) is a newly emerged decentralized machine learning
(ML) framework that combines on-device local training with server-based model
synchronization to train a centralized ML model over distributed nodes. In this
paper, we propose an asynchronous FL framework with periodic aggregation to
eliminate the straggler issue in FL systems. For the proposed model, we
investigate several device scheduling and update aggregation policies and
compare their performances when the devices have heterogeneous computation
capabilities and training data distributions. From the simulation results, we
conclude that the scheduling and aggregation design for asynchronous FL can be
rather different from the synchronous case. For example, a norm-based
significance-aware scheduling policy might not be efficient in an asynchronous
FL setting, and an appropriate "age-aware" weighting design for the model
aggregation can greatly improve the learning performance of such systems.
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