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Federated Learning (FL) in mobile environments faces significant communication bottlenecks.
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Jiaxiang Wu, Weidong Huang, Junzhou Huang and Tong Zhang · 2018
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“signSGD: Compressed Optimisation for Non-Convex Problems”
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli and Animashree Anandkumar · 2018
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“Speech commands: A dataset for limited-vocabulary speech recognition”
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“PowerSGD: Practical low-rank gradient compression for distributed optimization”
Thijs Vogels, Sai Karimireddy and Martin Jaggi · 2019
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“Qsparse-local-SGD: Distributed SGD with quantization, sparsification and local computations”
Debraj Basu, Deepesh Data, Can Karakus and Suhas Diggavi · 2019
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“Robust and communication-efficient federated learning from non-iid data”
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller and Wojciech Samek · 2019
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“Sparse binary compression: Towards distributed deep learning with minimal communication”
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller and Wojciech Samek · 2019
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“Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD”
Jianyu Wang and Gauri Joshi · 2019
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“The non-iid data quagmire of decentralized machine learning”
Kevin Hsieh, Amar Phanishayee, Onur Mutlu and Phillip Gibbons · 2020
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“Federated learning: Challenges, methods, and future directions”
Tian Li, Anit Sahu, Ameet Talwalkar and Virginia Smith · 2020
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“Tackling the objective inconsistency problem in heterogeneous federated optimization”
“Adaptive Gradient Communication via Critical Learning Regime Identification”
Saurabh Agarwal et al · 2021
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“Serverless empowered video analytics for ubiquitous networked cameras”
Miao Zhang et al · 2021
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“Delayed gradient averaging: Tolerate the communication latency for federated learning”
Ligeng Zhu et al · 2021
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“Fedzip: A compression framework for communication-efficient federated learning”
Amirhossein Malekijoo et al · 2021
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“Federated learning with compression: Unified analysis and sharp guarantees”
Farzin Haddadpour, Mohammad Kamani, Aryan Mokhtari and Mehrdad Mahdavi · 2021
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Jianyu Wang et al · 2020
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Tian Li et al · 2020
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“Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval”
Tobias Weyand, Andre Araujo, Bingyi Cao and Jack Sim · 2020
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“On communication compression for distributed optimization on heterogeneous data”
Sebastian Stich · 2020
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“Practical one-shot federated learning for cross-silo setting”
Qinbin Li, Bingsheng He and Dawn Song · 2020
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“Federated learning with matched averaging”
Hongyi Wang et al · 2020
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“Tackling the objective inconsistency problem in heterogeneous federated optimization”
Jianyu Wang et al · 2020
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“Smoothness matrices beat smoothness constants: Better communication compression techniques for distributed optimization”
Mher Safaryan, Filip Hanzely and Peter Richtárik · 2021
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“Federated Learning on Non-IID Data Silos: An Experimental Study”
Qinbin Li, Yiqun Diao, Quan Chen and Bingsheng He · 2022
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“Mixed-Precision Neural Network Quantization via Learned Layer-Wise Importance”
Chen Tang et al · 2022
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“Optimal rate adaption in federated learning with compressed communications”
Laizhong Cui, Xiaoxin Su, Yipeng Zhou and Jiangchuan Liu · 2022
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“Sharper convergence guarantees for asynchronous SGD for distributed and federated learning”
Anastasiia Koloskova, Sebastian Stich and Martin Jaggi · 2022
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“Accelerating Federated Learning via Parallel Servers: A Theoretically Guaranteed Approach”
Xuezheng Liu et al · 2022
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“Demystifying why local aggregation helps: Convergence analysis of hierarchical SGD”
Jiayi Wang, Shiqiang Wang, Rong-Rong Chen and Mingyue Ji · 2022
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“Statistical-based gradient compression method for distributed training system”, 2022
Ahmed. Abdelmoniem, Ahmed Elzanaty, Marco Canini and Mohamed-Slim Alouini · 2022
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“Theoretically better and numerically faster distributed optimization with smoothness-aware quantization techniques”
Bokun Wang, Mher Safaryan and Peter Richtárik · 2022
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“Learnings from federated learning in the real world”
Christophe Dupuy et al · 2022
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“Node selection toward faster convergence for federated learning on non-iid data”
Hongda Wu and Ping Wang · 2022
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“Sparks of artificial general intelligence: Early experiments with gpt-4”
Sébastien Bubeck et al · 2023
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