Decentralized Learning

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Decentralized learning studies how multiple devices or organizations can learn together while keeping data and computation local, which is useful when privacy, connectivity, or centralized infrastructure is limited. Methods include gossip averaging, decentralized stochastic gradient descent, knowledge distillation, random-walk updates, and communication-efficient model sharding, with applications from edge devices to large language model pretraining. Work evaluates these approaches on image, text, graph, and time-series benchmarks, as well as dynamic and wireless networks, and develops convergence and generalization guarantees. A central tension is balancing communication and computation costs with learning quality under non-IID data, changing network topologies, privacy requirements, and malicious participants.

Latest papers

101 papers11 papers in the last four weeks, down 39% on the four weeks before. 0.1% of all new papers.

Discount Yourself: Self-inclusive Freshness-weighted Gossip for Decentralised Learning under Client Outages
Oct 20, 2026Qiyuan Wang, Ke Xiao, Christos AnagnostopoulosFederated Learning Aggregation Decentralized Learning
University of Glasgow
PULSE: Personalized Opportunistic Learning for Wearable Devices
Oct 20, 2026Mattia Giovanni Campana, Dariush Salami, Hüseyin Yiğitler +1Personalized Learning Decentralized Learning
IIT-CNR · Aalto University
Learning to Refer: Inference-Time Collaboration in Decentralized Federated Learning
Oct 19, 2026Yuheng Ma, Chendi Wang, Qiong ZhangFederated Learning Decentralized Learning
KLATASDS-MOE, School of Statistics, East China Normal University · Department of Statistics and Data Science, Xiamen University · Division of Computing and Mathematical Sciences, MBZUAI
NBCLT: Parameter-Agnostic Norm-Ball Clipped Local Training
Oct 13, 2026Linxuan Pan, Shucheng Yang, Junchi YangCommunication-Efficient Distributed Training Decentralized Learning
Data-Topology Compatibility Enables Personalization in Decentralized Learning
Oct 10, 2026Veronica Xu, Lingfei Wang, Chenlei Leng +1Decentralized Learning Distributed Optimization
Multi-Token Random-Walk Learning via Meeting-Triggered Information Exchange
Oct 10, 2026Taichun Yeh, Xingran ChenDecentralized Learning Decentralized Optimization
Department of Electrical and Computer Engineering, Rutgers University, Piscataway Township, NJ 08854, USA · Engineering Systems and Design Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
Decentralized collaborative continual learning: A multi-objective minimization-based technique
Oct 7, 2026Yara Zgheib, Marc Antonini, Roula NassifDecentralized Learning Replay-Based Continual Learning
Universit´e Cˆote d’Azur, I3S Laboratory, CNRS, France
MetaLearnNCA: Few-Shot Offline Meta-Learning via Interacting Neural Cellular Automata
Oct 6, 2026Etienne Guichard, Stefano NicheleNeural Cellular Automata Meta-Learning
Department of Computer Science and Communication hiof.no
LFHE: Local-First Heuristic Evolution for Bounded Local Topology Search in Decentralized Learning with Non-IID Data
Oct 6, 2026Yin-Kuan Liang, Yan Gao, Yang LongGraph Structure Learning Decentralized Learning
Durham University · University of Cambridge
Population Scaling or Data Dilution? Dynamics of Local Topology Evolution in Decentralized Learning
Oct 4, 2026Yin-Kuan Liang, Yan Gao, Yang LongDistributed Optimization Decentralized Learning
Durham University · University of Cambridge
Unapologetically Distributed: A Call for Decentralized Document Analysis
Sep 30, 2026Adrià Molina, Oriol Ramos Terrades, Josep LladósOOD Generalization Decentralized Learning
Centre de Visió per Computador Universitat Autònoma de Barcelona Bellaterra, Catalonia · Computer Science Department Universitat Autònoma de Barcelona, Bellaterra, Catalonia
SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM
Sep 24, 2026Mengli Wei, Mengkai Zhu, Jiawen Chen +2Communication-Efficient Distributed Training Black-Box Optimization
School of Mathematics, Southeast University, Nanjing 210096, China
PROSE: A Theory of Optimal Stopping with Perishable Evidence for Peer Selection in Intermittently Connected Decentralised Learning
Sep 20, 2026Christos AnagnostopoulosOptimal Stopping Decentralized Learning
School of Computing Science, University of Glasgow, Glasgow, United Kingdom.
Serverless gossip training of LSTM failure detectors: A matched-protocol comparison with federated, local and centralized learning on NASA C-MAPSS
Sep 17, 2026Yusuf Öztürk, Enes Göktekin, Bengisu Atlı +3Decentralized Learning Fault Detection
Department of Electrical and Electronics Engineering, Antalya Bilim University, Antalya, Türkiye · Department of Computer Engineering, Antalya Bilim University, Antalya, Türkiye · Graduate School of Natural and Applied Sciences, Ankara University, Ankara, Türkiye +1
Constant Swap Regret in General-Sum Games via Two-Scale Higher-Order Optimism
Sep 15, 2026Tung MaiGame Theory Swap Regret
Adobe Research
Decentralized Gossip Learning and Federated Averaging for Histopathology Image Classification
Sep 15, 2026Yusuf Ozturk, Bengisu Atli, Enes Goktekin +2Federated Learning Aggregation Non-IID Federated Learning
Antalya Bilim University, Electrical and Electronics Engineering Dep. Antalya, Türkiye · Antalya Bilim University, Computer Engineering Dep. Antalya, Türkiye · Ankara University, Graduate School of Natural and Applied Sciences, Ankara, Türkiye +1
Privacy Preserving Gossip Learning
Sep 13, 2026Erkan Bayram, Mohamed-Ali Belabbas, Tamer BaşarContinual Learning Decentralized Learning
University of Illinois Urbana Champaign, IL, USA
Decentralized Optimal Equilibrium Learning Over Dynamic Networks
Sep 13, 2026Seref Taha Kiremitci, Muhammed O. SayinDecentralized Learning Game Theory
Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Türkiye.
Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks
Sep 12, 2026Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán +13Trustworthy AI 6G Networks
Byzantine-Robust Federated Fire Detection with a Rotating Coordinator
Sep 9, 2026Georgia Argyrou, Aymen Bahroun, Hedi Fendri +1Federated Learning Aggregation Byzantine-Robust Federated Learning
Department of Computer Science, Aalto University, Espoo, Finland · Kudelski Labs
Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration
Sep 7, 2026Xiao Ma, Hong Shen, Hui Tian +2Federated Learning Aggregation Byzantine-Robust Federated Learning
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration
Sep 7, 2026Xiao Ma, Hong Shen, Hui Tian +2Byzantine-Robust Federated Learning Federated Knowledge Distillation
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China · School of Engineering and Technology, Central Queensland University, Australia · School of Information and Communication Technology, Griffith University, Australia
Trust-But-Verify: Poisoning-Resilient Locally Private Graph Learning Protocols
Sep 7, 2026Longzhu He, Li Sun, Hao Peng +3Data Poisoning Attacks Decentralized Learning
Beijing University of Posts and Telecommunications · Beihang University · The Hong Kong University of Science and Technology +1
Optimal Rates for Agentic Networked Information Aggregation
Sep 4, 2026MohammadHossein Bateni, Zahra Hadizadeh, MohammadTaghi Hajiaghayi +2Statistical Learning Theory Decentralized Learning
Google Research · University of California, Irvine · University of Maryland
From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning
Sep 2, 2026Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria FodorFederated Graph Learning Decentralized Learning
School of Electrical Engineering and Computer Science, and Digital Futures KTH Royal Institute of Technology Stockholm, Sweden
Convergence Theory of Knowledge Distillation in Asynchronous P2P Gossip Learning Network
Sep 1, 2026Lucas Qingyang Fang, Tiyao Liu, Jinhao Jing +4Decentralized Learning Cross-Architecture Knowledge Distillation
University of California, Santa Cruz, USA · China University of Petroleum, China · The Chinese University of Hong Kong, Shenzhen, China +2
D-FROST: Decentralized Federated pRompt-tuning via Optimal tranSporT for Non-IID and Imbalanced Data
Sep 1, 2026Quan Minh Nguyen, Hoang M. Ngo, Trong Nghia Hoang +1Non-IID Federated Learning Decentralized Learning
University of Florida, FL, USA · Washington State University, WA, USA
Independent Reinforcement Learning in Discounted Markov Games
Sep 1, 2026Asrin Efe Yorulmaz, Ugur Aydin, Tamer BasarReinforcement Learning Game Theory
Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks
Aug 31, 2026Xingran Chen, Rohit Bhagat, Ghadir Ayache +3Fine-Tuning Communication-Efficient Distributed Training
Engineering Systems and Design Pillar, Singapore University of Technology and Design, Singapore 487372 · Department of Electrical and Computer Engineering, Rutgers University, Piscataway Township, NJ 08854, USA · LinkedIn, New York, NY 10118, USA +2
Decentralized Multitask Learning over Learned Task Graphs
Aug 27, 2026Zirui Wan, Stefan VlaskiGraph Structure Learning Multi-Task Learning
Department of Electrical and Electronic Engineering, Imperial College London, UK

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Discount Yourself: Self-inclusive Freshness-weighted Gossip for Decentralised Learning under Client Outages

Qiyuan Wang, Ke Xiao, Christos Anagnostopoulos

University of Glasgow

Federated Learning Aggregation Decentralized Learning Federated Learning

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Decentralised federated learning (DFL) trains personalised models on devices that average with their neighbours rather than with a central server. Real devices, however, are often offline for hours or days, and a client returning from an outage can recover only by aggregating the models its neighbourhood holds. We observe that the stalest of these models is usually the client's own, a case that existing staleness-aware rules, which discount only the models a client receives, do not address. We formalise aggregation after an outage as estimating the state of a fully available network and derive three principles. First, the receiver must be discounted like any neighbour. Every rule that weights it at least as much as each neighbour has an error that grows without bound with the outage, whereas discounting all candidates by relative staleness keeps the error bounded. Second, staleness must be measured as inherited lag rather than age, since local training resets a model's age but not the lag it absorbed from stale neighbours. We introduce the effective lag, which tracks this quantity at the cost of one scalar per message. Third, the discount must be steep, because absorbed lag persists in every later round. These principles define Self-inclusive Freshness-weighted Gossip (SFG), a family of drop-in aggregation rules with the communication and computation of decentralised federated averaging, whose guarantees hold for the stale receiver itself. On three image benchmarks and eight outage processes, including real smartphone availability traces, the lag-based members SFG-L and SFG-Cut consistently outperform decentralised federated averaging and recent age-of-information rules, and ablations show that each principle is necessary. The analysis also explains why tuned limited merging from gossip learning is effective.