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- (1)2023

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1. FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding NSTL国家科技图书文献中心

Yixuan Guan | Xuefeng Liu ... - 《IEEE INFOCOM 2024 - IEEE Conference on Computer Communications, Vol.2: Vancouver, British Columbia, Canada.20-23 May 2024》 - IEEE Conference on Computer Communications - 2024, - 821～830 - 共10页

摘要：Federated learning (FL) enables distributed training via periodically synchronizing model updates among participants. Communication overhead becomes a dominant constraint of FL since participating cli...

关键词： Federated Learning | Communication Overhead | Gradient Compression | Transform Coding

2. FedMDC: Enabling Communication-Efficient Federated Learning over Packet Lossy Networks via Multiple Description Coding NSTL国家科技图书文献中心

Yixuan Guan | Xuefeng Liu ... - 《2024 IEEE International Conference on Multimedia and Expo》 - IEEE International Conference on Multimedia and Expo - 2024, - 1～7 - 共7页

摘要：Federated learning (FL) generally suffers significant communication overhead from high-traffic gradient synchronization. The majority of existing studies on this problem aim at compressing gradients u...

关键词： Quantization (signal) | Federated learning | Distortion | Propagation losses | Encoding | Decoding | Reliability | Synchronization | Resource management | Resilience

3. Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing NSTL国家科技图书文献中心

Yixuan Guan | Xuefeng Liu ... - 《IEEE INFOCOM 2023 - IEEE Conference on Computer Communications: New York City, New York, USA, 17-20 May 2023, [v.1]》 - IEEE Conference on Computer Communications - 2023, - 261～270 - 共10页

摘要：Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices u...

关键词： Federated Learning | Communication Overhead | Gradient Compression | Distributed Compressed Sensing

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作者：Yixuan Guan

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