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Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization (2312.12863v2)

Published 20 Dec 2023 in cs.DC and cs.LG

Abstract: While providing machine learning model as a service to process users' inference requests, online applications can periodically upgrade the model utilizing newly collected data. Federated learning (FL) is beneficial for enabling the training of models across distributed clients while keeping the data locally. However, existing work has overlooked the coexistence of model training and inference under clients' limited resources. This paper focuses on the joint optimization of model training and inference to maximize inference performance at clients. Such an optimization faces several challenges. The first challenge is to characterize the clients' inference performance when clients may partially participate in FL. To resolve this challenge, we introduce a new notion of age of model (AoM) to quantify client-side model freshness, based on which we use FL's global model convergence error as an approximate measure of inference performance. The second challenge is the tight coupling among clients' decisions, including participation probability in FL, model download probability, and service rates. Toward the challenges, we propose an online problem approximation to reduce the problem complexity and optimize the resources to balance the needs of model training and inference. Experimental results demonstrate that the proposed algorithm improves the average inference accuracy by up to 12%.

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References (46)
  1. “Torchserve,” https://pytorch.org/serve.
  2. “Tensorflow serving,” https://github.com/tensorflow/serving.
  3. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics.   PMLR, 2017, pp. 1273–1282.
  4. P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al., “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021.
  5. P. Sun, H. Che, Z. Wang, Y. Wang, T. Wang, L. Wu, and H. Shao, “Pain-fl: Personalized privacy-preserving incentive for federated learning,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3805–3820, 2021.
  6. P. Sun, X. Chen, G. Liao, and J. Huang, “A profit-maximizing model marketplace with differentially private federated learning,” in IEEE INFOCOM 2022-IEEE Conference on Computer Communications.   IEEE, 2022, pp. 1439–1448.
  7. A. Imteaj, U. Thakker, S. Wang, J. Li, and M. H. Amini, “A survey on federated learning for resource-constrained iot devices,” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 1–24, 2021.
  8. Y. J. Cho, J. Wang, and G. Joshi, “Towards understanding biased client selection in federated learning,” in International Conference on Artificial Intelligence and Statistics.   PMLR, 2022, pp. 10 351–10 375.
  9. R. Zhou, J. Yu, R. Wang, B. Li, J. Jiang, and L. Wu, “A reinforcement learning approach for minimizing job completion time in clustered federated learning,” in IEEE INFOCOM, 2023.
  10. L. G. Ningning Ding and J. Huang, “Joint participation incentive and network pricing design for federated learning,” in IEEE INFOCOM, 2023.
  11. Z. Jiang, Y. Xu, H. Xu, Z. Wang, and C. Qian, “Heterogeneity-aware federated learning with adaptive client selection and gradient compression,” in IEEE INFOCOM, 2023.
  12. J. Liu, H. Xu, L. Wang, Y. Xu, C. Qian, J. Huang, and H. Huang, “Adaptive asynchronous federated learning in resource-constrained edge computing,” IEEE Transactions on Mobile Computing, vol. 22, no. 2, pp. 674–690, 2023.
  13. M. Salehi and E. Hossain, “Federated learning in unreliable and resource-constrained cellular wireless networks,” IEEE Transactions on Communications, vol. 69, no. 8, pp. 5136–5151, 2021.
  14. R. Saha, S. Misra, and P. K. Deb, “Fogfl: Fog-assisted federated learning for resource-constrained iot devices,” IEEE Internet of Things Journal, vol. 8, no. 10, pp. 8456–8463, 2021.
  15. Y. Fraboni, R. Vidal, L. Kameni, and M. Lorenzi, “Clustered sampling: Low-variance and improved representativity for clients selection in federated learning,” in ICML.   PMLR, 2021, pp. 3407–3416.
  16. H. Yang, M. Fang, and J. Liu, “Achieving linear speedup with partial worker participation in non-iid federated learning,” in ICLR, 2021.
  17. X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of fedavg on non-iid data,” in ICLR, 2020.
  18. H. H. Yang, A. Arafa, T. Q. S. Quek, and H. Vincent Poor, “Age-based scheduling policy for federated learning in mobile edge networks,” in IEEE ICASSP, 2020, pp. 8743–8747.
  19. K. Wang, Y. Ma, M. B. Mashhadi, C. H. Foh, R. Tafazolli, and Z. Ding, “Age of information in federated learning over wireless networks,” arXiv preprint arXiv:2209.06623, 2022.
  20. M. Ma, V. W. Wong, and R. Schober, “Aoi-driven client scheduling for federated learning: A lagrangian index approach.”
  21. S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1205–1221, 2019.
  22. Y. Liao, Y. Xu, H. Xu, L. Wang, and C. Qian, “Adaptive configuration for heterogeneous participants in decentralized federated learning,” in IEEE INFOCOM, 2023.
  23. F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-i.i.d. data,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 9, pp. 3400–3413, 2020.
  24. E. Gorbunov, K. P. Burlachenko, Z. Li, and P. Richtárik, “Marina: Faster non-convex distributed learning with compression,” in ICML.   PMLR, 2021, pp. 3788–3798.
  25. D. Alistarh, T. Hoefler, M. Johansson, N. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” Advances in Neural Information Processing Systems, vol. 31, 2018.
  26. S. U. Stich and S. P. Karimireddy, “The error-feedback framework: Better rates for sgd with delayed gradients and compressed communication,” Journal of Machine Learning Research, vol. 21, no. 237, p. 1–36, 2020.
  27. A. M. Parikshit Hegde, Gustavo de Veciana, “Network adaptive federated learning: Congestion and lossy compression,” in INFOCOM, 2023.
  28. P. Li, G. Cheng, X. Huang, J. Kang, R. Yu, Y. Wu, and M. Pan, “Anycostfl: Efficient on-demand federated learning over heterogeneous edge devices,” in IEEE INFOCOM, 2023.
  29. D. Wen, K.-J. Jeon, and K. Huang, “Federated dropout—a simple approach for enabling federated learning on resource constrained devices,” IEEE Wireless Communications Letters, vol. 11, no. 5, pp. 923–927, 2022.
  30. D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic, “Qsgd: Communication-efficient sgd via gradient quantization and encoding,” Advances in neural information processing systems, vol. 30, 2017.
  31. A. Reisizadeh, A. Mokhtari, H. Hassani, A. Jadbabaie, and R. Pedarsani, “Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization,” in International Conference on Artificial Intelligence and Statistics.   PMLR, 2020, pp. 2021–2031.
  32. F. H. Heting Liu and G. Cao, “Communication-efficient federated learning for heterogeneous edge devices based on adaptive gradient quantization,” in IEEE INFOCOM, 2023.
  33. J. Wang, H. Qi, A. S. Rawat, S. Reddi, S. Waghmare, F. X. Yu, and G. Joshi, “Fedlite: A scalable approach for federated learning on resource-constrained clients,” arXiv preprint arXiv:2201.11865, 2022.
  34. I. Kadota, A. Sinha, E. Uysal-Biyikoglu, R. Singh, and E. Modiano, “Scheduling policies for minimizing age of information in broadcast wireless networks,” IEEE/ACM Transactions on Networking, vol. 26, no. 6, pp. 2637–2650, 2018.
  35. M. K. Chowdhury Shisher, H. Qin, L. Yang, F. Yan, and Y. Sun, “The age of correlated features in supervised learning based forecasting,” in IEEE INFOCOM WKSHPS, 2021, pp. 1–8.
  36. S. Wang, J. Perazzone, M. Ji, and K. S. Chan, “Federated learning with flexible control,” in IEEE INFOCOM, 2023.
  37. B. Luo, X. Li, S. Wang, J. Huang, and L. Tassiulas, “Cost-effective federated learning in mobile edge networks,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3606–3621, 2021.
  38. J. Gorski, F. Pfeuffer, and K. Klamroth, “Biconvex sets and optimization with biconvex functions: a survey and extensions,” Mathematical methods of operations research, vol. 66, pp. 373–407, 2007.
  39. Y. Jiao, K. Yang, D. Song, and D. Tao, “Timeautoad: Autonomous anomaly detection with self-supervised contrastive loss for multivariate time series,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1604–1619, 2022.
  40. S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “SCAFFOLD: Stochastic controlled averaging for federated learning,” in ICML, ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119.   PMLR, 13–18 Jul 2020, pp. 5132–5143.
  41. D. Jhunjhunwala, S. Wang, and G. Joshi, “Fedexp: Speeding up federated averaging via extrapolation,” in ICLR, 2023.
  42. H. Yu, S. Yang, and S. Zhu, “Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning,” in AAAI, vol. 33, no. 01, 2019, pp. 5693–5700.
  43. J. Perazzone, S. Wang, M. Ji, and K. S. Chan, “Communication-efficient device scheduling for federated learning using stochastic optimization,” in IEEE INFOCOM.   IEEE, 2022, pp. 1449–1458.
  44. H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,” arXiv preprint arXiv:1708.07747, 2017.
  45. Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” 2011.
  46. A. Krizhevsky, G. Hinton et al., “Learning multiple layers of features from tiny images,” 2009.
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