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CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning

arXiv 2510.16694 ↗First posted 19 Oct 2025cs.LG cs.CR cs.DCcited by 0

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Abstract (arXiv)

Secure federated learning (FL) preserves data privacy during distributed model training. However, deploying such frameworks across heterogeneous devices results in performance bottlenecks, due to straggler clients with limited computational or network capabilities, slowing training for all participating clients. This paper introduces the first straggler mitigation technique for secure aggregation with deep neural networks. We propose CLIP, a client-side invariant neuron pruning technique coupled with network-aware pruning, that addresses compute and network bottlenecks due to stragglers during training with minimal accuracy loss. Our technique accelerates secure FL training by 13% to 34% across multiple datasets (CIFAR10, Shakespeare, FEMNIST) with an accuracy impact of between 1.3% improvement to 2.6% reduction.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 25 references that are in the map. First posted on arXiv 19 Oct 2025.

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19 of 25 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2019Deep Leakage from Gradientscited by 1,004
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  6. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  7. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  8. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  9. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  10. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  11. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  12. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 162
7 more
  1. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  2. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 60
  3. 2020Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneitycited by 56
  4. 2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
  5. 2022How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?cited by 19
  6. 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropoutcited by 16
  7. 2019Gap Aware Mitigation of Gradient Stalenesscited by 14

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