mlsanityData to 7 Oct 2026

Confederated Learning: Federated Learning with Decentralized Edge Servers

arXiv 2205.14905 ↗First posted 30 May 2022cs.LG cs.DC math.OCcited by 7

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Abstract (arXiv)

Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider a centralized framework, in which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner. Due to stringent communication and bandwidth constraints, such a centralized framework has limited scalability as the number of devices grows. To address this issue, in this paper, we propose a ConFederated Learning (CFL) framework. The proposed CFL consists of multiple servers, in which each server is connected with an individual set of devices as in the conventional FL framework, and decentralized collaboration is leveraged among servers to make full use of the data dispersed throughout the network. We develop an alternating direction method of multipliers (ADMM) algorithm for CFL. The proposed algorithm employs a random scheduling policy which randomly selects a subset of devices to access their respective servers at each iteration, thus alleviating the need of uploading a huge amount of information from devices to servers. Theoretical analysis is presented to justify the proposed method. Numerical results show that the proposed method can converge to a decent solution significantly faster than gradient-based FL algorithms, thus boasting a substantial advantage in terms of communication efficiency.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 30 of its 44 references that are in the map. First posted on arXiv 30 May 2022.

Builds on

30 of 44 references.

References · 30

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2014SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectivescited by 401
  6. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  7. 2019Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communicationcited by 218
  8. 2018Federated Learning via Over-the-Air Computationcited by 215
  9. 2014EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimizationcited by 160
  10. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  11. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 143
  12. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
18 more
  1. 2020FedSplit: An algorithmic framework for fast federated optimizationcited by 108
  2. 2019Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronizationcited by 106
  3. 2019Peer-to-peer Federated Learning on Graphscited by 85
  4. 2020Federated Accelerated Stochastic Gradient Descentcited by 85
  5. 2019Variance Reduced Local SGD with Lower Communication Complexitycited by 80
  6. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
  7. 2019Accelerating Federated Learning via Momentum Gradient Descentcited by 75
  8. 2017Collaborative Deep Learning in Fixed Topology Networkscited by 60
  9. 2013On the Linear Convergence of the ADMM in Decentralized Consensus Optimizationcited by 57
  10. 2021Decentralized Federated Learning: Balancing Communication and Computing Costscited by 54
  11. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  12. 2017Communication-Efficient Algorithms for Decentralized and Stochastic Optimizationcited by 42
  13. 2019Variance-Reduced Decentralized Stochastic Optimization with Accelerated Convergencecited by 33
  14. 2020Decentralized Federated Learning via SGD over Wireless D2D Networkscited by 32
  15. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  16. 2021Communication-Efficient ADMM-based Federated Learningcited by 14
  17. 2021FedHybrid: A Hybrid Primal-Dual Algorithm Framework for Federated Optimizationcited by 7
  18. 2019Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligencecited by 5

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 2.

7 papers.

  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  2. 2025FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Datacited by 4
  3. 2024Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approachcited by 2
  4. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  5. 2023Collaborative Distributed Machine Learningcited by 1
  6. 2024Fairness-Aware Job Scheduling for Multi-Job Federated Learningcited by 1
  7. 2025FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphscited by 1