mlsanityData to 7 Oct 2026

A Benchmark for Federated Hetero-Task Learning

arXiv 2206.03436 ↗First posted 7 Jun 2022cs.LGcited by 6

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Abstract (arXiv)

To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks. We also present B-FHTL, a federated hetero-task learning benchmark consisting of simulation dataset, FL protocols and a unified evaluation mechanism. B-FHTL dataset contains three well-designed federated learning tasks with increasing heterogeneity. Each task simulates the clients with different non-IID data and learning tasks. To ensure fair comparison among different FL algorithms, B-FHTL builds in a full suite of FL protocols by providing high-level APIs to avoid privacy leakage, and presets most common evaluation metrics spanning across different learning tasks, such as regression, classification, text generation and etc. Furthermore, we compare the FL algorithms in fields of federated multi-task learning, federated personalization and federated meta learning within B-FHTL, and highlight the influence of heterogeneity and difficulties of federated hetero-task learning. Our benchmark, including the federated dataset, protocols, the evaluation mechanism and the preliminary experiment, is open-sourced at https://github.com/alibaba/FederatedScope/tree/master/benchmark/B-FHTL

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 33 of its 52 references that are in the map. First posted on arXiv 7 Jun 2022.

Builds on

33 of 52 references.

References · 33

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  7. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2015Teaching Machines to Read and Comprehendcited by 1,039
  10. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  11. 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 998
  12. 2019Towards Federated Learning at Scale: System Designcited by 826
21 more
  1. 2018LEAF: A Benchmark for Federated Settingscited by 780
  2. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  3. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  4. 2017Federated Multi-Task Learningcited by 669
  5. 2019Unified Language Model Pre-training for Natural Language Understanding and Generationcited by 668
  6. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  7. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  8. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  10. 2019Fair Resource Allocation in Federated Learningcited by 389
  11. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  12. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  13. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  14. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  15. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  16. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  17. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  18. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  19. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  20. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  21. 2018Federated Learning for Keyword Spottingcited by 95

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Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 2.

6 papers.

  1. 2024FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Expertscited by 27
  2. 2023FedBone: Towards Large-Scale Federated Multi-Task Learningcited by 14
  3. 2025FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRAcited by 13
  4. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  5. 2025Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Taskscited by 4
  6. 2023HPN: Personalized Federated Hyperparameter Optimizationcited by 1