AI Chat AI Image Generator AI Video Text to Speech

Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation

08/01/2023

∙

by Tomas Ortega, et al.

∙

∙

Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability. However, it has a high communication cost, which has not been examined with quantized communications. To tackle this problem, we present a new algorithm (QAFeL), with a quantization scheme that establishes a shared "hidden" state between the server and clients to avoid the error propagation caused by direct quantization. This approach allows for high precision while significantly reducing the data transmitted during client-server interactions. We provide theoretical convergence guarantees for QAFeL and corroborate our analysis with experiments on a standard benchmark.

Tomas Ortega
2 publications
Hamid Jafarkhani
37 publications

research

∙ 03/26/2021

Hierarchical Quantized Federated Learning: Convergence Analysis and System Design

Federated learning is a collaborative machine learning framework to trai...

0 Lumin Liu, et al. ∙

research

∙ 06/01/2023

CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning

Asynchronous federated learning aims to solve the straggler problem in h...

0 Xiang Ma, et al. ∙

research

∙ 10/03/2022

Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation

Synchronous updates may compromise the efficiency of cross-device federa...

7 Mohammad Taha Toghani, et al. ∙

research

∙ 02/23/2021

QuPeL: Quantized Personalization with Applications to Federated Learning

Traditionally, federated learning (FL) aims to train a single global mod...

18 Kaan Ozkara, et al. ∙

research

∙ 11/27/2021

Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression

Many assumptions in the federated learning literature present a best-cas...

0 Francois Gauthier, et al. ∙

research

∙ 07/17/2020

Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise

The feasibility of federated learning is highly constrained by the serve...

0 Marten van Dijk, et al. ∙

research

∙ 09/18/2023

A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning

Communication efficiency is a major challenge in federated learning (FL)...

0 Pedro Valdeira, et al. ∙