A-LAQ: Adaptive Lazily Aggregated Quantized Gradient
Author:
Affiliation:
1. Network and Systems Engineering, Electrical Engineering and Computer Science KTH Royal Institute of Technology,Stockholm,Sweden
2. Ericsson,Stockholm,Sweden
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10008390/10008474/10008580.pdf?arnumber=10008580
Reference20 articles.
1. Adaptive Federated Learning With Gradient Compression in Uplink NOMA
2. Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning
3. A Novel Adaptive Gradient Compression Approach for Communication-Efficient Federated Learning
4. Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
5. ALAQ: Adaptive lazily aggregated quantized gradient;mahmoudi;diva2 1685948,2022
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1. Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study;Applied Sciences;2024-03-24
2. A Communication-Efficient Adaptive Algorithm for Federated Learning Under Cumulative Regret;IEEE Transactions on Signal Processing;2024
3. Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks;IEEE Transactions on Wireless Communications;2023
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