Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning (2405.09394v2)

Published 15 May 2024 in cs.LG and cs.DC

Abstract: Federated Learning (FL) is a collaborative machine learning paradigm for training models on local sensitive data with privacy protection. Pre-trained transformer-based models have emerged as useful foundation models (FMs) to be fine-tuned for a wide range of downstream tasks. However, large-scale pre-trained models make it challenging for traditional FL due to high communication overhead in the resource-constrained IoT. This has inspired the field of parameter-efficient fine-tuning (PEFT) research. Existing PEFT methods attempt to optimize model performance at the given dropout level. Such an approach places the burden on human users to find a dropout rate that provides a satisfactory level of performance through trial-and-error, which is time consuming and resource intensive. To address this limitation, we propose the Step-wise Parameter Dropout for Continual Federated Learning (SPD-CFL) approach. Instead of pre-defining a desired dropout rate, it allows users to specify the target level of performance and then attempts to find the most suitable dropout rate for the given FL model. Specifically, on the server side, SPD-CFL drops trainable parameters in a stepwise manner to improve communication efficiency by reducing the rank of low-rank adaptation (LoRA). The sensitivity-based gradient consistency (SGC) measure is designed to facilitate the adaptive adjustment of parameter dropout. In addition, SPD-CFL introduces continual learning (CL) on the client side to mitigate performance degradation due to the inconsistent optima with distinct parameter dropout rates under heterogeneous FL. Extensive experiments on the public benchmark dataset CIFAR-10 and a real-world medical Face dataset demonstrate significant superiority of SPD-CFL over state-of-the-art methods. Compared to the best-performing baseline, it achieves a 2.07% higher test AUC while reducing communication overhead by 29.53%.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. Communication and computation efficiency in federated learning: A survey. Internet of Things, page 100742, 2023.
  2. Federated learning with personalization layers. arXiv preprint arXiv:1912.00818, 2019.
  3. Slora: Federated parameter efficient fine-tuning of language models. arXiv preprint arXiv:2308.06522, 2023.
  4. Fedadapter: Efficient federated learning for modern nlp. arXiv preprint arXiv:2205.10162, 2022.
  5. Conv-adapter: Exploring parameter efficient transfer learning for convnets. arXiv preprint arXiv:2208.07463, 2022.
  6. On the importance and applicability of pre-training for federated learning. In The Eleventh International Conference on Learning Representations, 2022.
  7. Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation. arXiv preprint arXiv:2309.08842, 2023.
  8. Cf-vit: A general coarse-to-fine method for vision transformer. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 7042–7052, 2023.
  9. Heterogeneous lora for federated fine-tuning of on-device foundation models. In International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023, 2023.
  10. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009.
  11. Sparse networks from scratch: Faster training without losing performance. arXiv preprint arXiv:1907.04840, 2019.
  12. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
  13. Fedmbp: Multi-branch prototype federated learning on heterogeneous data. In 2023 IEEE International Conference on Image Processing (ICIP), pages 2180–2184. IEEE, 2023.
  14. Cross-attention is all you need: Adapting pretrained transformers for machine translation. arXiv preprint arXiv:2104.08771, 2021.
  15. Pre-trained models: Past, present and future. AI Open, 2:225–250, 2021.
  16. Parameter-efficient transfer learning for nlp. In International Conference on Machine Learning, pages 2790–2799. PMLR, 2019.
  17. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
  18. Fact: Factor-tuning for lightweight adaptation on vision transformer. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 1060–1068, 2023.
  19. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2):1–210, 2021.
  20. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016.
  21. Learning multiple layers of features from tiny images. 2009.
  22. Gmp*: Well-tuned global magnitude pruning can outperform most bert-pruning methods. arXiv preprint arXiv:2210.06384, 2022.
  23. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems, 2:429–450, 2020.
  24. Scaling down to scale up: A guide to parameter-efficient fine-tuning. arXiv preprint arXiv:2303.15647, 2023.
  25. Ds-transunet: Dual swin transformer u-net for medical image segmentation. IEEE Transactions on Instrumentation and Measurement, 71:1–15, 2022.
  26. Query2label: A simple transformer way to multi-label classification. arXiv preprint arXiv:2107.10834, 2021.
  27. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022, 2021.
  28. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017.
  29. St-adapter: Parameter-efficient image-to-video transfer learning. Advances in Neural Information Processing Systems, 35:26462–26477, 2022.
  30. Few-round learning for federated learning. Advances in Neural Information Processing Systems, 34:28612–28622, 2021.
  31. Rethinking architecture design for tackling data heterogeneity in federated learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10061–10071, 2022.
  32. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
  33. Conquering the communication constraints to enable large pre-trained models in federated learning. arXiv, 2022.
  34. Federated learning from pre-trained models: A contrastive learning approach. Advances in Neural Information Processing Systems, 35:19332–19344, 2022.
  35. Scheduling optimization of linear schedule with constraint programming. Computer-Aided Civil and Infrastructure Engineering, 33(2):124–151, 2018.
  36. Fedbert: When federated learning meets pre-training. ACM Transactions on Intelligent Systems and Technology (TIST), 13(4):1–26, 2022.
  37. Fedlora: Model-heterogeneous personalized federated learning with lora tuning. arXiv preprint arXiv:2310.13283, 2023.
  38. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. arXiv preprint arXiv:2106.10199, 2021.
  39. Increlora: Incremental parameter allocation method for parameter-efficient fine-tuning. arXiv preprint arXiv:2308.12043, 2023.
  40. Adaptive budget allocation for parameter-efficient fine-tuning. arXiv preprint arXiv:2303.10512, 2023.
  41. Distilled one-shot federated learning. arXiv preprint arXiv:2009.07999, 2020.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets