mlsanityData to 14 Oct 2026

Smart Multi-tenant Federated Learning

arXiv 2207.04202 ↗First posted 9 Jul 2022cs.LG cs.AI cs.CVcited by 1

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Abstract (arXiv)

Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous training activities could overload resource-constrained devices. In this work, we propose a smart multi-tenant FL system, MuFL, to effectively coordinate and execute simultaneous training activities. We first formalize the problem of multi-tenant FL, define multi-tenant FL scenarios, and introduce a vanilla multi-tenant FL system that trains activities sequentially to form baselines. Then, we propose two approaches to optimize multi-tenant FL: 1) activity consolidation merges training activities into one activity with a multi-task architecture; 2) after training it for rounds, activity splitting divides it into groups by employing affinities among activities such that activities within a group have better synergy. Extensive experiments demonstrate that MuFL outperforms other methods while consuming 40% less energy. We hope this work will inspire the community to further study and optimize multi-tenant FL.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 50 of its 73 references that are in the map. First posted on arXiv 9 Jul 2022.

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50 of 73 references.

References · 50

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  6. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  7. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  8. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  9. 2017A Survey on Multi-Task Learningcited by 840
  10. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
  11. 2019Towards Federated Learning at Scale: System Designcited by 832
  12. 2018How To Backdoor Federated Learningcited by 765
38 more
  1. 2017Federated Multi-Task Learningcited by 672
  2. 2018Taskonomy: Disentangling Task Transfer Learningcited by 634
  3. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  4. 2020Federated Learning with Matched Averagingcited by 564
  5. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  6. 2016Cross-stitch Networks for Multi-task Learningcited by 467
  7. 2019Which Tasks Should Be Learned Together in Multi-task Learning?cited by 375
  8. 2020Adaptive Personalized Federated Learningcited by 358
  9. 2021Efficiently Identifying Task Groupings for Multi-Task Learningcited by 222
  10. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  11. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  12. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  13. 2017Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Artcited by 182
  14. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  15. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 169
  16. 2019AdaShare: Learning What To Share For Efficient Deep Multi-Task Learningcited by 160
  17. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 149
  18. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 118
  19. 2020Learning to Branch for Multi-Task Learningcited by 115
  20. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 109
  21. 2020TiFL: A Tier-based Federated Learning Systemcited by 96
  22. 2016Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classificationcited by 93
  23. 2019Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloadscited by 79
  24. 2019Branched Multi-Task Networks: Deciding What Layers To Sharecited by 76
  25. 2022Divergence-aware Federated Self-Supervised Learningcited by 73
  26. 2021Collaborative Unsupervised Visual Representation Learning from Decentralized Datacited by 69
  27. 2018NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Visioncited by 68
  28. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  29. 2018Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotationscited by 51
  30. 2020Performance Optimization for Federated Person Re-identification via Benchmark Analysiscited by 50
  31. 2018A Modulation Module for Multi-task Learning with Applications in Image Retrievalcited by 49
  32. 2021Stronger Privacy for Federated Collaborative Filtering with Implicit Feedbackcited by 26
  33. 2016Integrated perception with recurrent multi-task neural networkscited by 24
  34. 2019Federated Learning for Ranking Browser History Suggestionscited by 24
  35. 2021EasyFL: A Low-code Federated Learning Platform For Dummiescited by 20
  36. 2021Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identificationcited by 20
  37. 2018GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learningcited by 17
  38. 2022Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysiscited by 9

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Citing papers per year: 2023: 1.

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  1. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 84