mlsanityData to 7 Oct 2026

Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications

arXiv 2102.08503 ↗First posted 16 Feb 2021cs.LGcited by 84

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Abstract (arXiv)

We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML systems, primarily for the purpose of personalizing these systems. In recent years, support for an additional federated task has been added: federated learning (FL) of deep neural networks. To our knowledge, only one other system has been described in literature that supports FL at scale. We include comparisons to that system to help discuss design decisions and attached trade-offs. Finally, we describe two specific large scale personalization use cases in detail to showcase the applicability of federated tuning to on-device personalization and to highlight application specific solutions.

Key facts. Cited by 84 papers in the map (data through 7 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 16 Feb 2021.

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16 of 33 references.

References · 16

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  6. 2019Towards Federated Learning at Scale: System Designcited by 826
  7. 2017Federated Multi-Task Learningcited by 669
  8. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  9. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  10. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  11. 2018Protection Against Reconstruction and Its Applications in Private Federated Learningcited by 127
  12. 2016Decentralized Collaborative Learning of Personalized Models over Networkscited by 73
4 more
  1. 2019Variational Federated Multi-Task Learningcited by 69
  2. 2020Improving on-device speaker verification using federated learning with privacycited by 29
  3. 2016Personalized Speech recognition on mobile devicescited by 23
  4. 2018DJAM: distributed Jacobi asynchronous method for learning personal modelscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 23; 2023: 23; 2024: 12; 2025: 8; 2026: 5.

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  1. 2021A Field Guide to Federated Optimizationcited by 312
  2. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  3. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 116
  4. 2022Neurotoxin: Durable Backdoors in Federated Learningcited by 97
  5. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
  6. 2021On Large-Cohort Training for Federated Learningcited by 73
  7. 2022Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Modelscited by 52
  8. 2023(Amplified) Banded Matrix Factorization: A unified approach to private trainingcited by 49
  9. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  10. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  11. 2023On the Convergence of Federated Averaging with Cyclic Client Participationcited by 31
  12. 2023Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learningcited by 29
38 more
  1. 2022POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Pagingcited by 26
  2. 2021Smart at what cost? Characterising Mobile Deep Neural Networks in the wildcited by 25
  3. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  4. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  5. 2023Practical Differentially Private and Byzantine-resilient Federated Learningcited by 18
  6. 2022Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneitycited by 17
  7. 2023FLINT: A Platform for Federated Learning Integrationcited by 17
  8. 2023Federated Prompt Learning for Weather Foundation Models on Devicescited by 17
  9. 2024Federated Learning in Practice: Reflections and Projectionscited by 15
  10. 2024FedMef: Towards Memory-efficient Federated Dynamic Pruningcited by 14
  11. 2025POPri: Private Federated Learning using Preference-Optimized Synthetic Datacited by 14
  12. 2022PrivFairFL: Privacy-Preserving Group Fairness in Federated Learningcited by 13
  13. 2025Do We Really Need to Design New Byzantine-robust Aggregation Rules?cited by 13
  14. 2021SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learningcited by 11
  15. 2023FedWon: Triumphing Multi-domain Federated Learning Without Normalizationcited by 11
  16. 2022Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusioncited by 10
  17. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
  18. 2023Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learningcited by 9
  19. 2023ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacycited by 9
  20. 2024DrJAX: Scalable and Differentiable MapReduce Primitives in JAXcited by 9
  21. 2021FRL: Federated Rank Learningcited by 8
  22. 2022Pisces: Efficient Federated Learning via Guided Asynchronous Trainingcited by 8
  23. 2022Auxo: Efficient Federated Learning via Scalable Client Clusteringcited by 8
  24. 2023Green Federated Learningcited by 8
  25. 2024pfl-research: simulation framework for accelerating research in Private Federated Learningcited by 8
  26. 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysiscited by 8
  27. 2021FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspectivecited by 7
  28. 2022Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learningcited by 7
  29. 2021Revisiting IoT Device Identificationcited by 6
  30. 2022Federated Domain Adaptation for ASR with Full Self-Supervisioncited by 6
  31. 2023Monitoring and Adapting ML Models on Mobile Devicescited by 6
  32. 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
  33. 2024Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learningcited by 5
  34. 2021Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradientcited by 4
  35. 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationcited by 4
  36. 2022Certified Robustness in Federated Learningcited by 4
  37. 2022FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learningcited by 4
  38. 2022FedorAS: Federated Architecture Search under system heterogeneitycited by 4

50 most cited of 84 All 84 (JSON)