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Federated Frank-Wolfe Algorithm

arXiv 2408.10090 ↗First posted 19 Aug 2024cs.LG cs.DCcited by 0

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Abstract (arXiv)

Federated learning (FL) has gained a lot of attention in recent years for building privacy-preserving collaborative learning systems. However, FL algorithms for constrained machine learning problems are still limited, particularly when the projection step is costly. To this end, we propose a Federated Frank-Wolfe Algorithm (FedFW). FedFW features data privacy, low per-iteration cost, and communication of sparse signals. In the deterministic setting, FedFW achieves an varepsilon-suboptimal solution within O(varepsilon-2) iterations for smooth and convex objectives, and O(varepsilon-3) iterations for smooth but non-convex objectives. Furthermore, we present a stochastic variant of FedFW and show that it finds a solution within O(varepsilon-3) iterations in the convex setting. We demonstrate the empirical performance of FedFW on several machine learning tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 39 of its 54 references that are in the map. First posted on arXiv 19 Aug 2024.

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References · 39

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  6. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  7. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  9. 2021A Field Guide to Federated Optimizationcited by 312
  10. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  11. 2020Is Local SGD Better than Minibatch SGD?cited by 151
  12. 2020Minibatch vs Local SGD for Heterogeneous Distributed Learningcited by 147
27 more
  1. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 143
  2. 2020FedSplit: An algorithmic framework for fast federated optimizationcited by 108
  3. 2019Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronizationcited by 106
  4. 2020Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithmscited by 78
  5. 2022Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakagecited by 72
  6. 2016Convergence Rate of Frank-Wolfe for Non-Convex Objectivescited by 59
  7. 2020Federated Composite Optimizationcited by 49
  8. 2018Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximizationcited by 47
  9. 2016Variance-Reduced and Projection-Free Stochastic Optimizationcited by 40
  10. 2016Stochastic Frank-Wolfe Methods for Nonconvex Optimizationcited by 40
  11. 2022FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participationcited by 35
  12. 2021FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimizationcited by 29
  13. 2013New Analysis and Results for the Frank-Wolfe Methodcited by 17
  14. 2018A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programmingcited by 14
  15. 2020Stochastic Frank-Wolfe for Constrained Finite-Sum Minimizationcited by 14
  16. 2018Decentralized Submodular Maximization: Bridging Discrete and Continuous Settingscited by 13
  17. 2022Fast Composite Optimization and Statistical Recovery in Federated Learningcited by 12
  18. 2016Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problemscited by 11
  19. 2019A Conditional Gradient-Based Augmented Lagrangian Frameworkcited by 10
  20. 2021Frank-Wolfe and friends: a journey into projection-free first-order optimization methodscited by 10
  21. 2019Stochastic Frank-Wolfe for Composite Convex Minimizationcited by 9
  22. 2015A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimizationcited by 8
  23. 2018Frank-Wolfe Splitting via Augmented Lagrangian Methodcited by 7
  24. 2017A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Normcited by 4
  25. 2023Federated Learning with Convex Global and Local Constraintscited by 4
  26. 2014Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithmscited by 3
  27. 2022Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimizationcited by 1

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