Data to 7 Oct 2026

Decentralized Collaborative Learning of Personalized Models over Networks

arXiv 1610.05202 ↗First posted 17 Oct 2016cs.LG cs.AI cs.DCcited by 73

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Abstract (arXiv)

We consider a set of learning agents in a collaborative peer-to-peer network, where each agent learns a personalized model according to its own learning objective. The question addressed in this paper is: how can agents improve upon their locally trained model by communicating with other agents that have similar objectives? We introduce and analyze two asynchronous gossip algorithms running in a fully decentralized manner. Our first approach, inspired from label propagation, aims to smooth pre-trained local models over the network while accounting for the confidence that each agent has in its initial model. In our second approach, agents jointly learn and propagate their model by making iterative updates based on both their local dataset and the behavior of their neighbors. To optimize this challenging objective, our decentralized algorithm is based on ADMM.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Oct 2016.

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Citing papers per year: 2020: 1; 2021: 18; 2022: 19; 2023: 13; 2024: 12; 2025: 7; 2026: 3.

73 papers.

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2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
2021Personalized Federated Learning through Local Memorizationcited by 57
2021Decentralized Federated Learning: Balancing Communication and Computing Costscited by 54
2021Gradient-Leakage Resilient Federated Learningcited by 40
2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
2023Personalized Federated Learning under Mixture of Distributionscited by 37
2022Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Datacited by 31
2021A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularizationcited by 27
2024FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Expertscited by 27

38 more

2021Decentralized Local Stochastic Extra-Gradient for Variational Inequalitiescited by 25
2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
2020Specialized federated learning using a mixture of expertscited by 23
2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
2021Preserved central model for faster bidirectional compression in distributed settingscited by 21
2021Meta-HAR: Federated Representation Learning for Human Activity Recognitioncited by 21
2021Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Developmentcited by 21
2023Personalized Decentralized Federated Learning with Knowledge Distillationcited by 21
2022Self-Aware Personalized Federated Learningcited by 16
2023AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devicescited by 15
2021D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learningcited by 12
2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
2021Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applicationscited by 11
2023Backdoor Attacks in Peer-to-Peer Federated Learningcited by 9
2021Separation of Powers in Federated Learningcited by 8
2022FedVLN: Privacy-preserving Federated Vision-and-Language Navigationcited by 8
2022FedDAR: Federated Domain-Aware Representation Learningcited by 8
2022Personalized Federated Learning via Heterogeneous Modular Networkscited by 8
2024Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clientscited by 7
2022TEE-based decentralized recommender systems: The raw data sharing redemptioncited by 6
2023Stochastic Unrolled Federated Learningcited by 6
2022Network Gradient Descent Algorithm for Decentralized Federated Learningcited by 5
2022Collaborative Algorithms for Online Personalized Mean Estimationcited by 5
2023From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learningcited by 5
2023DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representationscited by 5
2024Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clientscited by 5
2025FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learningcited by 5
2021Implicit Model Specialization through DAG-based Decentralized Federated Learningcited by 4
2022A review of Federated Learning in Intrusion Detection Systems for IoTcited by 4
2022Asynchronous Hierarchical Federated Learningcited by 4
2023Efficient Node Selection in Private Personalized Decentralized Learningcited by 4
2023Noncommutative C^*-algebra Net: Learning Neural Networks with Powerful Product Structure in C^*-algebracited by 4
2023Topology-Based Reconstruction Prevention for Decentralised Learningcited by 4
2025EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-Tuningcited by 4
2021Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systemscited by 3
2022Scalable Collaborative Learning via Representation Sharingcited by 3
2021Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descentcited by 2
2022GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimationcited by 2

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