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Fast Federated Learning by Balancing Communication Trade-Offs
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.laa.2005.07.021openalex-snapshot
Fast low-rank modifications of the thin singular value decomposition
inferredcites →https://doi.org/10.1109/comst.2020.2970550openalex-snapshot
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/ijcnn.2019.8852172openalex-snapshot
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
inferredcites →https://doi.org/10.1109/jsac.2019.2904348openalex-snapshot
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
inferredcites →https://doi.org/10.1109/tbdata.2015.2472014openalex-snapshot
Petuum: A New Platform for Distributed Machine Learning on Big Data
inferredcites →https://doi.org/10.1109/tnnls.2019.2944481openalex-snapshot
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
inferredcites →https://doi.org/10.1137/18m1183480openalex-snapshot
Why Are Big Data Matrices Approximately Low Rank?
inferredcites →https://doi.org/10.21437/interspeech.2014-274openalex-snapshot
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs
inferredcites →https://doi.org/10.48550/arxiv.1602.05629openalex-snapshot
Communication-Efficient Learning of Deep Networks from Decentralized Data
inferredcites →https://doi.org/10.48550/arxiv.1610.02132openalex-snapshot
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
inferredcites →https://doi.org/10.48550/arxiv.1610.05492openalex-snapshot
Federated Learning: Strategies for Improving Communication Efficiency
inferredcites →https://doi.org/10.48550/arxiv.1703.03400openalex-snapshot
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
inferredcites →https://doi.org/10.48550/arxiv.1705.07878openalex-snapshot
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1706.02677openalex-snapshot
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
inferredcites →https://doi.org/10.48550/arxiv.1712.01887openalex-snapshot
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
inferredcites →https://doi.org/10.48550/arxiv.1806.04090openalex-snapshot
ATOMO: Communication-efficient Learning via Atomic Sparsification
inferredcites →https://doi.org/10.48550/arxiv.1806.08054openalex-snapshot
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
inferredcites →https://doi.org/10.48550/arxiv.1808.07217openalex-snapshot
Don't Use Large Mini-Batches, Use Local SGD
inferredcites →https://doi.org/10.48550/arxiv.1808.07576openalex-snapshot
Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
inferredcites →https://doi.org/10.48550/arxiv.1910.06378openalex-snapshot
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Energy and Loss-aware Selective Updating for SplitFed Learning with Energy Harvesting-Powered Devices
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Communication-efficient federated learning via knowledge distillation
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Top-K Difference Sparsification and Quantization for Communication-Efficient Model Aggregation in Wireless Ad Hoc Federated Learning
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Distributed Deep Reinforcement Learning-Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
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Joint Resource Optimization for Federated Edge Learning With Integrated Sensing, Communication, and Computation
inferred← citeshttps://doi.org/10.1109/jsac.2022.3211564openalex-snapshot
AAFL: Asynchronous-Adaptive Federated Learning in Edge-Based Wireless Communication Systems for Countering Communicable Infectious Diseasess
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inferred← citeshttps://doi.org/10.1109/mnet.006.2200651openalex-snapshot
Communication-Efficient and Byzantine-Robust Federated Learning for Mobile Edge Computing Networks
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Trustworthy Federated Learning: A Comprehensive Review, Architecture, Key Challenges, and Future Research Prospects
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Improving Model Accuracy in Federated Learning with Mish Activation Function and the FedAvg-RAdam Optimization Algorithm
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Heterogeneous Computation and Resource Allocation for Wireless Powered Federated Edge Learning Systems
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Quantized and Asynchronous Federated Learning
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Communication-Efficient Semi-Decentralized Federated Learning in the Presence of Stragglers
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Adaptive Control of Local Updating and Model Compression for Efficient Federated Learning
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REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning Over Mobile Devices
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CPPer-FL: Clustered Parallel Training for Efficient Personalized Federated Learning
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Heterogeneity-Aware Cooperative Federated Edge Learning With Adaptive Computation and Communication Compression
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Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
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Cost-Efficient and Secure Federated Learning for Edge Computing
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Knowledge- and Model-Driven Deep Reinforcement Learning for Efficient Federated Edge Learning: Single- and Multi-Agent Frameworks
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Communication-Efficient Hybrid Federated Learning for E-Health With Horizontal and Vertical Data Partitioning
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An Empirical Evaluation of the Data Leakage in Federated Graph Learning
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Adaptive Local Update and Neural Composition for Accelerating Federated Learning in Heterogeneous Edge Networks
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Flexible Synchronization Control for Accurate and Efficient Federated Learning
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Communication-Efficient Federated Edge Learning via Optimal Probabilistic Device Scheduling
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Joint Client Scheduling and Quantization Optimization in Energy Harvesting-Enabled Federated Learning Networks
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Adaptive Communication Control for Efficient Hierarchical Federated Learning in Heterogeneous Edge Networks
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A Meta-Learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning
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DAG Blockchain-based Personalized Federated Mutual learning in Internet of Vehicles
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DAG Blockchain-based Personalized Federated Mutual learning in Internet of Vehicles
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FedCGS: Leverage Feature Compression and Conditional GAN for Privacy Protection and Efficient Communication in Federated Learning
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