To tackle this problem, we propose FedPulse, a Partial Training (PT) based mechanism to mitigate the effect of stragglers in FL. The idea is to reduce the ...
Nov 3, 2024 · Federated Learning is a widely adopted method to train neural networks over distributed data. One main limitation is the performance degradation ...
Abstract—Federated Learning (FL) allows distributed devices, known as clients, to train Machine Learning (ML) models.
Mar 14, 2024 · We propose different client latency distributions in order to understand how FL algorithms utilize additional training examples and novel data ...
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What is federated learning with additional mechanisms on clients to reduce communication costs?
Which of the following training methods consist of having the trainee assume the attitudes and behavior of others?
In this work, we explore how the number of clients sampled at each round (the cohort size) impacts the quality of the learned model and the training dynamics of ...
This paper proposes a new approach, TimelyFL, to handling stragglers in federated learning (FL). The approach combines several ideas: adapting the duration of ...
Apr 14, 2023 · To overcome this barrier, we propose TimelyFL, a heterogeneity-aware asynchronous. FL framework with adaptive partial training. During the.
Missing: Mechanism | Show results with:Mechanism
Consequently, FedSAE can significantly reduce stragglers in highly heterogeneous systems. We incorporate Active Learning into FedSAE to dynamically schedule ...
In this paper, we focus on system heterogeneity in federated learning and leverage the interplay between statistical accu- racy and system heterogeneity to ...
A novel technique to reduce the communication costs associated with federated learning that optimizes both server-client communications and computation costs.