mlsanityData to 7 Oct 2026

Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

arXiv 2311.03154 ↗First posted 6 Nov 2023cs.LGcited by 23

Not on the map. Why?

Abstract (arXiv)

There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: (i) parallel FL (PFL), where clients train models in a parallel manner; and (ii) sequential FL (SFL), where clients train models in a sequential manner. In contrast to that of PFL, the convergence theory of SFL on heterogeneous data is still lacking. In this paper, we establish the convergence guarantees of SFL for strongly/general/non-convex objectives on heterogeneous data. The convergence guarantees of SFL are better than that of PFL on heterogeneous data with both full and partial client participation. Experimental results validate the counterintuitive analysis result that SFL outperforms PFL on extremely heterogeneous data in cross-device settings.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2023.

Builds on

0 of 0 references.

No references here yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 12; 2026: 5.

23 papers.

Sort:

By area:

  1. 2024OpenFGL: A Comprehensive Benchmark for Federated Graph Learningcited by 16
  2. 2024One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversitycited by 8
  3. 2025FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimationcited by 7
  4. 2024The Impact of Cut Layer Selection in Split Federated Learningcited by 6
  5. 2024Parallel Split Learning with Global Samplingcited by 3
  6. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  7. 2024When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devicescited by 2
  8. 2025Data Heterogeneity and Forgotten Labels in Split Federated Learningcited by 2
  9. 2025Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Managementcited by 1
  10. 2025SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learningcited by 1
  11. 2026EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networkscited by 1
  12. 2024UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Taskscited by 0
11 more
  1. 2025Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Samplingcited by 0
  2. 2025SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillationcited by 0
  3. 2025orb-QFL: Orbital Quantum Federated Learningcited by 0
  4. 2025PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecturecited by 0
  5. 2025Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimizationcited by 0
  6. 2025DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learningcited by 0
  7. 2025Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Thingscited by 0
  8. 2026FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learningcited by 0
  9. 2026Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attentioncited by 0
  10. 2026UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Modelscited by 0
  11. 2026Heterogeneity-Aware Dataset Scheduling for Efficient Audio Large Language Model Trainingcited by 0