mlsanityData to 7 Oct 2026

SPIDER: Searching Personalized Neural Architecture for Federated Learning

arXiv 2112.13939 ↗First posted 27 Dec 2021cs.LG cs.CVcited by 13

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Abstract (arXiv)

Federated learning (FL) is an efficient learning framework that assists distributed machine learning when data cannot be shared with a centralized server due to privacy and regulatory restrictions. Recent advancements in FL use predefined architecture-based learning for all the clients. However, given that clients' data are invisible to the server and data distributions are non-identical across clients, a predefined architecture discovered in a centralized setting may not be an optimal solution for all the clients in FL. Motivated by this challenge, in this work, we introduce SPIDER, an algorithmic framework that aims to Search Personalized neural architecture for federated learning. SPIDER is designed based on two unique features: (1) alternately optimizing one architecture-homogeneous global model (Supernet) in a generic FL manner and one architecture-heterogeneous local model that is connected to the global model by weight sharing-based regularization (2) achieving architecture-heterogeneous local model by a novel neural architecture search (NAS) method that can select optimal subnet progressively using operation-level perturbation on the accuracy value as the criterion. Experimental results demonstrate that SPIDER outperforms other state-of-the-art personalization methods, and the searched personalized architectures are more inference efficient.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 29 of its 34 references that are in the map. First posted on arXiv 27 Dec 2021.

Builds on

29 of 34 references.

References · 29

  1. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  2. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  3. 2020Adaptive Federated Optimizationcited by 1,036
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  5. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  8. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  9. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  10. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  11. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 480
  12. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
17 more
  1. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  2. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  3. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  4. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  5. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  6. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  7. 2019Federated Evaluation of On-device Personalizationcited by 147
  8. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  9. 2020A Survey on Evolutionary Neural Architecture Searchcited by 142
  10. 2021Rethinking Architecture Selection in Differentiable NAScited by 92
  11. 2021HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmarkcited by 83
  12. 2020DSNAS: Direct Neural Architecture Search without Parameter Retrainingcited by 69
  13. 2020Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Taskscited by 43
  14. 2020Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Searchcited by 30
  15. 2020Real-time Federated Evolutionary Neural Architecture Searchcited by 20
  16. 2020Federated Neural Architecture Searchcited by 10
  17. 2020Differentially-private Federated Neural Architecture Searchcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 2; 2025: 3; 2026: 2.

13 papers.

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  1. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  2. 2023Pruning Deep Neural Networks from a Sparsity Perspectivecited by 25
  3. 2025Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probingcited by 14
  4. 2024SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overheadcited by 9
  5. 2022Supernet Training for Federated Image Classification under System Heterogeneitycited by 4
  6. 2022FedorAS: Federated Architecture Search under system heterogeneitycited by 4
  7. 2023Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systemscited by 3
  8. 2022A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Searchcited by 2
  9. 2025FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architecturescited by 2
  10. 2024DynamicFL: Federated Learning with Dynamic Communication Resource Allocationcited by 1
  11. 2025DPFNAS: Differential Privacy-Enhanced Federated Neural Architecture Search for 6G Edge Intelligencecited by 0
  12. 2026Optimized Federated Knowledge Distillation with Distributed Neural Architecture Searchcited by 0
1 more
  1. 2026OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligencecited by 0