mlsanityData to 14 Oct 2026

An Optimization Framework for Federated Edge Learning

arXiv 2111.13526 ↗First posted 26 Nov 2021cs.LGcited by 3

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Abstract (arXiv)

The optimal design of federated learning (FL) algorithms for solving general machine learning (ML) problems in practical edge computing systems with quantized message passing remains an open problem. This paper considers an edge computing system where the server and workers have possibly different computing and communication capabilities and employ quantization before transmitting messages. To explore the full potential of FL in such an edge computing system, we first present a general FL algorithm, namely GenQSGD, parameterized by the numbers of global and local iterations, mini-batch size, and step size sequence. Then, we analyze its convergence for an arbitrary step size sequence and specify the convergence results under three commonly adopted step size rules, namely the constant, exponential, and diminishing step size rules. Next, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint, with the focus on the overall implementing process of FL. Specifically, for any given step size sequence under each considered step size rule, we optimize the numbers of global and local iterations and mini-batch size to optimally implement FL for applications with preset step size sequences. We also optimize the step size sequence along with these algorithm parameters to explore the full potential of FL. The resulting optimization problems are challenging non-convex problems with non-differentiable constraint functions. We propose iterative algorithms to obtain KKT points using general inner approximation (GIA) and tricks for solving complementary geometric programming (CGP). Finally, we numerically demonstrate the remarkable gains of GenQSGD with optimized algorithm parameters over existing FL algorithms and reveal the significance of optimally designing general FL algorithms.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 26 Nov 2021.

Builds on

21 of 37 references.

References · 21

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2021Convex optimizationcited by 2,515
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  6. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  7. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 278
  8. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  9. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  10. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  11. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  12. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
9 more
  1. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 95
  2. 2018Federated Learning for Keyword Spottingcited by 95
  3. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 72
  4. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  5. 2020A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performancecited by 26
  6. 2019Cell-Free Massive MIMO for Wireless Federated Learningcited by 25
  7. 2019Federated Learning for Ranking Browser History Suggestionscited by 24
  8. 2021Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCAcited by 3
  9. 2021Optimization-Based GenQSGD for Federated Edge Learningcited by 3

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  2. 2022Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learningcited by 1
  3. 2023GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systemscited by 1

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  3. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
  4. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design
  5. 2024FedFQ: Federated Learning with Fine-Grained Quantization
  6. 2026Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning
  7. 2025Lightweight Federated Learning over Wireless Edge Networks
  8. 2022Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing