Heterogeneity-Aware Adaptive Federated Learning Scheduling-Reference-Cited by-同舟云学术

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Heterogeneity-Aware Adaptive Federated Learning Scheduling

Published:2022-12-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Big Data (Big Data)
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Author:

Han Jingoo¹,Khan Ahmad Faraz¹,Zawad Syed²,Anwar Ali³,Angel Nathalie Baracaldo⁴,Zhou Yi⁴,Yan Feng²,Butt Ali R.¹

Affiliation:

1. Virginia Tech

2. University of Nevada,Reno

3. University of Minnesota

4. IBM Research–Almaden

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020721.pdf?arnumber=10020721

Reference27 articles.

1. On the convergence of fedavg on non-iid data;li;International Conference on Learning Representations,2020

2. Heterogeneity-aware federated learning;yang,2020

3. Oort: Efficient federated learning via guided participant selection;lai;Proc USENIX OSDI,2021

4. Federated learning with non-iid data;zhao,2018

5. Advances and open problems in federated learning;kairouz,2019

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1. Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge Computing Networks;ICC 2024 - IEEE International Conference on Communications;2024-06-09

2. FLOAT: Federated Learning Optimizations with Automated Tuning;Proceedings of the Nineteenth European Conference on Computer Systems;2024-04-22

3. Counterfactual Explanations and Federated Learning for Enhanced Data Analytics Optimisation;Springer Tracts in Nature-Inspired Computing;2024

4. Towards cost-effective and resource-aware aggregation at Edge for Federated Learning;2023 IEEE International Conference on Big Data (BigData);2023-12-15

5. A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning;Future Internet;2023-06-08

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