mlsanityData to 7 Oct 2026

A Survey of Federated Evaluation in Federated Learning

arXiv 2305.08070 ↗First posted 14 May 2023cs.LG cs.DCcited by 7

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Abstract (arXiv)

In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), which is called federated evaluation in this work. This is because clients do not expose their original data to preserve data privacy. Federated evaluation plays a vital role in client selection, incentive mechanism design, malicious attack detection, etc. In this paper, we provide the first comprehensive survey of existing federated evaluation methods. Moreover, we explore various applications of federated evaluation for enhancing FL performance and finally present future research directions by envisioning some challenges.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 32 of its 74 references that are in the map. First posted on arXiv 14 May 2023.

Builds on

32 of 74 references.

References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2018Federated Learning with Non-IID Datacited by 1,099
  4. 2016HyperNetworkscited by 1,003
  5. 2021Model-Contrastive Federated Learningcited by 762
  6. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  7. 2021Towards Personalized Federated Learningcited by 462
  8. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  9. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 279
  10. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
  11. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  12. 2018Mitigating Sybils in Federated Learning Poisoningcited by 158
20 more
  1. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  2. 2018Blockchained On-Device Federated Learningcited by 105
  3. 2020TiFL: A Tier-based Federated Learning Systemcited by 95
  4. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 95
  5. 2019Reliable Federated Learning for Mobile Networkscited by 92
  6. 2019Measure Contribution of Participants in Federated Learningcited by 78
  7. 2021Personalized Federated Learning through Local Memorizationcited by 57
  8. 2020Federated learning with class imbalance reductioncited by 52
  9. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  10. 2020FOCUS: Dealing with Label Quality Disparity in Federated Learningcited by 36
  11. 2021A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparisoncited by 31
  12. 2018Joint Service Pricing and Cooperative Relay Communication for Federated Learningcited by 22
  13. 2021Improving Fairness for Data Valuation in Horizontal Federated Learningcited by 22
  14. 2018Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learningcited by 17
  15. 2021Transparent Contribution Evaluation for Secure Federated Learning on Blockchaincited by 16
  16. 2021FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systemscited by 16
  17. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  18. 2022A Survey on Participant Selection for Federated Learning in Mobile Networkscited by 11
  19. 2020Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Frameworkcited by 7
  20. 2022Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sourcescited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.

  1. 2025Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generationcited by 7
  2. 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
  3. 2025Multi-Stakeholder Alignment in LLM-Powered Collaborative AI Systems: A Multi-Agent Framework for Intelligent Tutoringcited by 2
  4. 2026Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Datacited by 1
  5. 2024FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectificationcited by 0
  6. 2024Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shiftscited by 0
  7. 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspectivecited by 0