mlsanityData to 14 Oct 2026

One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them

arXiv 1905.04634 ↗First posted 12 May 2019cs.LG cs.DC stat.MLcited by 23

Not on the map. Why?

Abstract (arXiv)

We consider distributed statistical optimization in one-shot setting, where there are m machines each observing n i.i.d. samples. Based on its observed samples, each machine sends a B-bit-long message to a server. The server then collects messages from all machines, and estimates a parameter that minimizes an expected convex loss function. We investigate the impact of communication constraint, B, on the expected error and derive a tight lower bound on the error achievable by any algorithm. We then propose an estimator, which we call Multi-Resolution Estimator (MRE), whose expected error (when B≥log mn) meets the aforementioned lower bound up to poly-logarithmic factors, and is thereby order optimal. We also address the problem of learning under tiny communication budget, and present lower and upper error bounds when B is a constant. The expected error of MRE, unlike existing algorithms, tends to zero as the number of machines (m) goes to infinity, even when the number of samples per machine (n) remains upper bounded by a constant. This property of the MRE algorithm makes it applicable in new machine learning paradigms where m is much larger than n.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 5; 2024: 3; 2025: 3; 2026: 3.

23 papers.

Sort:

By area:

  1. 2022FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learningcited by 66
  2. 2023One-Shot Federated Conformal Predictioncited by 27
  3. 2022One-shot Federated Learning without Server-side Trainingcited by 20
  4. 2022Non-IID Quantum Federated Learning with One-shot Communication Complexitycited by 15
  5. 2021FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learningcited by 12
  6. 2021Federated Few-Shot Learning with Adversarial Learningcited by 11
  7. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  8. 2024Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Modelscited by 7
  9. 2022A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environmentscited by 3
  10. 2022CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approachcited by 2
  11. 2025Towards One-shot Federated Learning: Advances, Challenges, and Future Directionscited by 2
  12. 2025Efficient Federated Learning with Heterogeneous Data and Adaptive Dropoutcited by 2
11 more
  1. 2024Harnessing Federated Generative Learning for Green and Sustainable Internet of Thingscited by 1
  2. 2025FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devicescited by 1
  3. 2026One-Shot Federated Ridge Regression: Exact Recovery via Sufficient Statistic Aggregationcited by 1
  4. 2021Order Optimal Bounds for One-Shot Federated Learning over non-Convex Loss Functionscited by 0
  5. 2022HEX: Human-in-the-loop Explainability via Deep Reinforcement Learningcited by 0
  6. 2023FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybridcited by 0
  7. 2023Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hullscited by 0
  8. 2023Distributed Continual Learning with CoCoA in High-dimensional Linear Regressioncited by 0
  9. 2024Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregationcited by 0
  10. 2026CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learningcited by 0
  11. 2026Structural Entropy-Driven Graph Diffusion Generation for One-Shot Federated Graph Learningcited by 0