Communication-efficient distributed learning: An overview

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Advances and Open Problems in Federated Learning

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Distributed Subgradient Methods for Multi-Agent Optimization

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Adaptive Federated Learning in Resource Constrained Edge Computing Systems

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Constrained Consensus and Optimization in Multi-Agent Networks

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A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

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EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization

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Online Learning and Online Convex Optimization

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QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

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Distributed Optimization Over Time-Varying Directed Graphs

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Energy Efficient Federated Learning Over Wireless Communication Networks

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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

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Federated Learning via Over-the-Air Computation

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Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs

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Client-Edge-Cloud Hierarchical Federated Learning

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Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory

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Federated Learning over Wireless Networks: Optimization Model Design and Analysis

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Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

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Edge artificial intelligence for 6g: vision, enabling technologies, and applications

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On the Linear Convergence of the ADMM in Decentralized Consensus Optimization

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Introduction to Online Convex Optimization

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Broadband Analog Aggregation for Low-Latency Federated Edge Learning

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TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep LearningPreprint

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Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning

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Federated Learning Over Wireless Fading Channels

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A Learning-Based Incentive Mechanism for Federated Learning

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Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach

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FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

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Sparse Communication for Distributed Gradient Descent

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Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation

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Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air

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Scalable distributed DNN training using commodity GPU cloud computing

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Sparsified SGD with Memory

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PowerSGD: Practical Low-Rank Gradient Compression for Distributed OptimizationPreprint

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Federated learning over wireless networks Convergence analysis and resource allocation

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Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective

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Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks

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Quantized incremental algorithms for distributed optimization

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Convergence Time Optimization for Federated Learning Over Wireless Networks

Chen, M., Poor, H. V., Saad, W., & Cui, S. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3042530 · Cited by 403 · see citing works

Push–Pull Gradient Methods for Distributed Optimization in Networks

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Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning

Shi, W., Zhou, S., Niu, Z., Jiang, M., & Geng, L. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3025446 · Cited by 381 · see citing works

One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis

Zhu, G., Du, Y., Gunduz, D., & Huang, K. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3039309 · Cited by 376 · see citing works

Gradient Sparsification for Communication-Efficient Distributed OptimizationPreprint

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Optimizing Federated Learning in Distributed Industrial IoT: A Multi-Agent Approach

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Multitask Diffusion Adaptation Over Networks

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Toward an Automated Auction Framework for Wireless Federated Learning Services Market

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UVeQFed: Universal Vector Quantization for Federated Learning

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cpSGD: Communication-efficient and differentially-private distributed SGDPreprint

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Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties

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Qsparse-local-SGD: Distributed SGD with quantization, sparsification, and local computations

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Diffusion LMS Over Multitask Networks

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ATOMO: Communication-efficient Learning via Atomic SparsificationPreprint

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Over-the-Air Federated Learning From Heterogeneous Data

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Accelerated Distributed Nesterov Gradient Descent

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Event-triggered zero-gradient-sum distributed consensus optimization over directed networks

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FedSA: A Semi-Asynchronous Federated Learning Mechanism in Heterogeneous Edge Computing

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Communication Compression for Decentralized Training

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Federated Learning via Intelligent Reflecting Surface

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Communication-Efficient Distributed Dual Coordinate AscentPreprint

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DLM: Decentralized Linearized Alternating Direction Method of Multipliers

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Design and Analysis of Uplink and Downlink Communications for Federated Learning

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Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach

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Communication-efficient algorithms for decentralized and stochastic optimization

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Distributed Online Convex Optimization on Time-Varying Directed Graphs

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Distributed subgradient methods and quantization effects

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Federated Learning: A signal processing perspective

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Network Newton Distributed Optimization Methods

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Optimized Power Control Design for Over-the-Air Federated Edge Learning

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A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks

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A Saddle Point Algorithm for Networked Online Convex Optimization

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Quantized Subgradient Algorithm and Data-Rate Analysis for Distributed Optimization

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Optimal Contract Design for Efficient Federated Learning With Multi-Dimensional Private Information

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Trading Regret for Efficiency: Online Convex Optimization with Long Term ConstraintsPreprint

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Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks

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Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation

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Event-Triggered Communication and Data Rate Constraint for Distributed Optimization of Multiagent Systems

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Semi-Decentralized Federated Learning With Cooperative D2D Local Model Aggregations

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Asynchronous Distributed Optimization With Event-Driven Communication

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Decentralized Dynamic Optimization Through the Alternating Direction Method of Multipliers

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Dynamic Scheduling for Over-the-Air Federated Edge Learning With Energy Constraints

Sun, Y., Zhou, S., Niu, Z., & Gündüz, D. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126078 · Cited by 143 · see citing works

A linear speedup analysis of distributed deep learning with sparse and quantized communication

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An Exact Quantized Decentralized Gradient Descent Algorithm

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Quantized Federated Learning Under Transmission Delay and Outage Constraints

Wang, Y., Xu, Y., Shi, Q.-J., & Chang, T.-H. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126081 · Cited by 138 · see citing works

Communication Complexity of Distributed Convex Learning and Optimization

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Is Local SGD Better than Minibatch SGD?

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Event-Triggered Quantized Communication-Based Distributed Convex Optimization

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Local SGD with Periodic Averaging: Tighter Analysis and Adaptive SynchronizationPreprint

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A Regularization Approach to Learning Task Relationships in Multitask Learning

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SparCML

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Decentralized Quasi-Newton Methods

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Distributed Subgradient Method With Edge-Based Event-Triggered Communication

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High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning

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DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated CompressionPreprint

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Low-Latency Federated Learning Over Wireless Channels With Differential Privacy

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Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms

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Event-triggered distributed optimization in sensor networks

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DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers

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Lower Bounds and Optimal Algorithms for Personalized Federated LearningPreprint

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Distributed Consensus Optimization in Multiagent Networks With Time-Varying Directed Topologies and Quantized Communication

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Cost-Effective Federated Learning in Mobile Edge Networks

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eSGD: Communication Efficient Distributed Deep Learning on the Edge

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Communication-Censored ADMM for Decentralized Consensus Optimization

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SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

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Distributed Zero-Order Algorithms for Nonconvex Multiagent Optimization

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Distributed Event-Triggered Gradient Method for Constrained Convex Minimization

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Susquehanna Chorale Spring Concert "Roots and Wings"

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