Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning
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Abstract (arXiv)
Federated Learning (FL) is gaining widespread interest for its ability to share knowledge while preserving privacy and reducing communication costs. Unlike Centralized FL, Decentralized FL (DFL) employs a network architecture that eliminates the need for a central server, allowing direct communication among clients and leading to significant communication resource savings. However, due to data heterogeneity, not all neighboring nodes contribute to enhancing the local client's model performance. In this work, we introduce textbfAFIND+, a simple yet efficient algorithm for sampling and aggregating neighbors in DFL, with the aim of leveraging collaboration to improve clients' model performance. AFIND+ identifies helpful neighbors, adaptively adjusts the number of selected neighbors, and strategically aggregates the sampled neighbors' models based on their contributions. Numerical results on real-world datasets with diverse data partitions demonstrate that AFIND+ outperforms other sampling algorithms in DFL and is compatible with most existing DFL optimization algorithms.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 37 of its 56 references that are in the map. First posted on arXiv 5 Jul 2024.
Builds on
37 of 56 references.
References · 37
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2016Deep Learning with Differential Privacycited by 2,682
- 2018LEAF: A Benchmark for Federated Settingscited by 780
- 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
- 2021Towards Personalized Federated Learningcited by 462
- 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 361
- 2021A Field Guide to Federated Optimizationcited by 312
- 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
- 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
- 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
- 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
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- 2020Survey of Personalization Techniques for Federated Learningcited by 144
- 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
- 2021Decentralized Federated Averagingcited by 114
- 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
- 2019Peer-to-peer Federated Learning on Graphscited by 85
- 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
- 2021Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Datacited by 69
- 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
- 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
- 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
- 2023A Survey on Decentralized Federated Learningcited by 27
- 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
- 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
- 2021Decentralized federated learning of deep neural networks on non-iid datacited by 23
- 2021Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunitiescited by 22
- 2023Personalized Decentralized Federated Learning with Knowledge Distillationcited by 21
- 2023AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networkscited by 21
- 2021Decentralized Wireless Federated Learning with Differential Privacycited by 18
- 2022Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoIcited by 16
- 2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
- 2023IronForge: An Open, Secure, Fair, Decentralized Federated Learningcited by 15
- 2021Decentralized Personalized Federated Learning: Lower Bounds and Optimal Algorithm for All Personalization Modescited by 13
- 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
- 2022Fast Heterogeneous Federated Learning with Hybrid Client Selectioncited by 8
- 2022Confederated Learning: Federated Learning with Decentralized Edge Serverscited by 7
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.