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Fast Convergence Rates for Distributed Non-Bayesian Learning

arXiv 1508.05161 ↗First posted 21 Aug 2015math.OCcited by 26

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Abstract (arXiv)

We consider the problem of distributed learning, where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a distributed algorithm and establish consistency, as well as a non-asymptotic, explicit and geometric convergence rate for the concentration of the beliefs around the set of optimal hypotheses. Additionally, if the agents interact over static networks, we provide an improved learning protocol with better scalability with respect to the number of nodes in the network.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Aug 2015.

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Citing papers per year: 2021: 6; 2022: 2; 2023: 8; 2024: 6; 2025: 4.

26 papers.

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  1. 2021Improving the Transient Times for Distributed Stochastic Gradient Methodscited by 13
  2. 2021Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networkscited by 10
  3. 2021Communication-efficient Distributed Cooperative Learning with Compressed Beliefscited by 4
  4. 2021Learning from Heterogeneous Data Based on Social Interactions over Graphscited by 4
  5. 2021Scalable Average Consensus with Compressed Communicationscited by 3
  6. 2024Submodular Information Selection for Hypothesis Testing with Misclassification Penaltiescited by 3
  7. 2022FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differencescited by 2
  8. 2022Distributed Bayesian Learning of Dynamic Statescited by 2
  9. 2023Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphscited by 2
  10. 2021Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphscited by 1
  11. 2023On the Fusion Strategies for Federated Decision Makingcited by 1
  12. 2023Non-Asymptotic Performance of Social Machine Learning Under Limited Datacited by 1
14 more
  1. 2023Causal Influences over Social Learning Networkscited by 1
  2. 2023Distributed Variational Inference for Online Supervised Learningcited by 1
  3. 2023Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densitiescited by 1
  4. 2024Causal Influence in Federated Edge Inferencecited by 1
  5. 2024An Optimal Transport Approach for Network Regressioncited by 1
  6. 2023An Asynchronous Decentralized Algorithm for Wasserstein Barycenter Problemcited by 0
  7. 2023Differentially Private Distributed Estimation and Learningcited by 0
  8. 2024Distributed Fractional Bayesian Learning for Adaptive Optimizationcited by 0
  9. 2024Learning Multimodal Confidence for Intention Recognition in Human-Robot Interactioncited by 0
  10. 2024Classification with a Network of Partially Informative Agents: Enabling Wise Crowds from Individually Myopic Classifierscited by 0
  11. 2025Robust Information Selection for Hypothesis Testing with Misclassification Penaltiescited by 0
  12. 2025Doubly Adaptive Social Learningcited by 0
  13. 2025Adaptive Learning for IRS-Assisted Wireless Networks: Securing Opportunistic Communications Against Byzantine Eavesdropperscited by 0
  14. 2025A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networkscited by 0