mlsanityData to 13 Oct 2026

IPLS : A Framework for Decentralized Federated Learning

arXiv 2101.01901 ↗First posted 6 Jan 2021cs.NI cs.DC cs.LGcited by 26

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Abstract (arXiv)

The proliferation of resourceful mobile devices that store rich, multidimensional and privacy-sensitive user data motivate the design of federated learning (FL), a machine-learning (ML) paradigm that enables mobile devices to produce an ML model without sharing their data. However, the majority of the existing FL frameworks rely on centralized entities. In this work, we introduce IPLS, a fully decentralized federated learning framework that is partially based on the interplanetary file system (IPFS). By using IPLS and connecting into the corresponding private IPFS network, any party can initiate the training process of an ML model or join an ongoing training process that has already been started by another party. IPLS scales with the number of participants, is robust against intermittent connectivity and dynamic participant departures/arrivals, requires minimal resources, and guarantees that the accuracy of the trained model quickly converges to that of a centralized FL framework with an accuracy drop of less than one per thousand.

Key facts. Cited by 26 papers in the map (data through 13 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 6 Jan 2021.

Builds on

12 of 22 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,273
  3. 2019Federated Machine Learning: Concept and Applicationscited by 411
  4. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 394
  5. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 351
  6. 2019Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communicationcited by 220
  7. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  8. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 117
  9. 2014IPFS - Content Addressed, Versioned, P2P File Systemcited by 69
  10. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 64
  11. 2019BAFFLE : Blockchain Based Aggregator Free Federated Learningcited by 38
  12. 2020SplitEasy: A Practical Approach for Training ML models on Mobile Devicescited by 10

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 5; 2022: 3; 2023: 8; 2024: 5; 2025: 2; 2026: 3.

26 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  3. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  4. 2023A Survey on Decentralized Federated Learningcited by 27
  5. 2022On the (In)security of Peer-to-Peer Decentralized Machine Learningcited by 23
  6. 2024Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchorscited by 21
  7. 2023Decentralized Learning Made Easy with DecentralizePycited by 18
  8. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  9. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  10. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  11. 2022Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learningcited by 4
  12. 2021DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topologycited by 3
14 more
  1. 2023P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setupscited by 3
  2. 2021DeceFL: A Principled Decentralized Federated Learning Frameworkcited by 2
  3. 2021Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descentcited by 2
  4. 2023Sparse Decentralized Federated Learningcited by 2
  5. 2024Decentralized Intelligence Network (DIN)cited by 2
  6. 2026Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federationcited by 2
  7. 2023Decentralized Personalized Online Federated Learningcited by 1
  8. 2025FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classificationcited by 1
  9. 2026ERIS: Enhancing the Privacy-Utility-Scalability Trade-Off in Federated Learning via Federated Shard Aggregationcited by 1
  10. 2023Towards Sybil Resilience in Decentralized Learningcited by 0
  11. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  12. 2024Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communicationcited by 0
  13. 2025DFCA: Decentralized Federated Clustering Algorithmcited by 0
  14. 2026Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspectivecited by 0