References
- Introducing Federated Learning into Internet of Things ecosystems – preliminary considerations, 07 2022.
- A. Bellet, A. Kermarrec, and E. Lavoie, “Dcliques: Compensating noniidness in decentralized federated learning with topology”, CoRR, vol. abs/2104.07365, 2021.
- K. Bogacka, A. Danilenka, and K. Wasielewska-Michniewska, “Diagnosing machine learning problems in federated learning systems: A case study”. In: M. Ganzha, L. Maciaszek, M. Paprzycki, and D. Ślęzak, eds., Proceedings of the 18th Conference on Computer Science and Intelligence Systems, vol. 35, 2023, 871–876, 10.15439/2023F722.
- Q. Cheng and G. Long, “Federated learning operations (flops): Challenges, lifecycle and approaches”. In: 2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2022, 12–17, 10.1109/TAAI57707.2022.00012.
- L. Chou, Z. Liu, Z. Wang, and A. Shrivastava, “Efficient and less centralized federated learning”, CoRR, vol. abs/2106.06627, 2021.
- A.-I. Consortium. “D7.2 Pilot Scenario Implementation – First Version”, 2022.
- X. Du, X. Chen, J. Cao, M. Wen, S.-C. Cheung, and H. Jin, “Understanding the bug characteristics and fix strategies of federated learning systems”. In: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, New York, NY, USA, 2023, 1358–1370, 10.1145/3611643.3616347.
- S. Duan, C. Liu, P. Han, X. Jin, X. Zhang, X. Xiang, H. Pan, et al., “Fed-dnn-debugger: Automatically debugging deep neural network models in federated learning”, Security and Communication Networks, vol. 2023, 2023.
- H. Eichner, T. Koren, H. B. McMahan, N. Srebro, and K. Talwar, “Semi-cyclic stochastic gradient descent”, CoRR, vol. abs/1904.10120, 2019.
- A. Ghosh, J. Chung, D. Yin, and K. Ramchandran. “An efficient framework for clustered federated learning”, 2021.
- W. Gill, A. Anwar, and M. A. Gulzar. “Feddebug: Systematic debugging for federated learning applications”, 2023.
- X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks”. In: Y. W. Teh and M. Titterington, eds., Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, vol. 9, Chia Laguna Resort, Sardinia, Italy, 2010, 249–256.
- F. Godin, J. Degrave, J. Dambre, and W. De Neve, “Dual rectified linear units (drelus): A replacement for tanh activation functions in quasirecurrent neural networks”, Pattern Recognition Letters, vol. 116, 2018, 8–14.
- B. Hanin, “Which neural net architectures give rise to exploding and vanishing gradients?”. In: S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, eds., Advances in Neural Information Processing Systems, vol. 31, 2018.
- Harshvardhan, A. Ghosh, and A. Mazumdar. “An improved algorithm for clustered federated learning”, 2022.
- I. Hegedűs, G. Danner, and M. Jelasity, “Gossip learning as a decentralized alternative to federated learning”. In: J. Pereira and L. Ricci, eds., Distributed Applications and Interoperable Systems, Cham, 2019, 74–90.
- L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning for internet of things: Recent advances, taxonomy, and open challenges”, CoRR, vol. abs/2009.13012, 2020.
- D. Kreuzberger, N. Kühl, and S. Hirschl, “Machine learning operations (mlops): Overview, definition, and architecture”, IEEE Access, vol. 11, 2023, 31866–31879, 10.1109/ACCESS.2023.3262138.
- J. Lee, J. Oh, S. Lim, S. Yun, and J. Lee, “Tornadoaggregate: Accurate and scalable federated learning via the ring-based architecture”, CoRR, vol. abs/2012.03214, 2020.
- A. Li, R. Liu, M. Hu, L. A. Tuan, and H. Yu, “Towards interpretable federated learning”, arXiv preprint arXiv:2302.13473, 2023.
- A. Li, L. Zhang, J. Wang, F. Han, and X.-Y. Li, “Privacy-preserving efficient federated-learning model debugging”, IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 10, 2022, 2291–2303, 10.1109/TPDS.2021.3137321.
- Q. Li, Z. Wen, Z. Wu, S. Hu, N. Wang, Y. Li, X. Liu, and B. He, “A survey on federated learning systems: Vision, hype and reality for data privacy and protection”, IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, 2023, 3347–3366, 10.1109/TKDE.2021.3124599.
- L. Liu, J. Zhang, S. Song, and K. B. Letaief, “Edgeassisted hierarchical federated learning with non-iid data”, CoRR, vol. abs/1905.06641, 2019.
- Y. Liu, W. Wu, L. Flokas, J. Wang, and E. Wu, “Enabling sql-based training data debugging for federated learning”, CoRR, vol. abs/2108.11884, 2021.
- H. B. McMahan, E. Moore, D. Ramage, and B. A. y Arcas, “Federated learning of deep networks using model averaging”, CoRR, vol. abs/1602.05629, 2016.
- L. Meng, Y. Wei, R. Pan, S. Zhou, J. Zhang, and W. Chen, “Vadaf: Visualization for abnormal client detection and analysis in federated learning”, ACM Trans. Interact. Intell. Syst., vol. 11, no. 3–4, 2021, 10.1145/3426866.
- M. A. Mercioni and S. Holban, “The most used activation functions: Classic versus current”. In: 2020 International Conference on Development and Application Systems (DAS), 2020, 141–145.
- N. Mhaisen, A. A. Abdellatif, A. Mohamed, A. Erbad, and M. Guizani, “Optimal user-edge assignment in hierarchical federated learning based on statistical properties and network topology constraints”, IEEE Transactions on Network Science and Engineering, vol. 9, no. 1, 2022, 55–66, 10.1109/TNSE.2021.3053588.
- R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks”. In: International conference on machine learning, 2013, 1310–1318.
- G. Philipp, “The nonlinearity coefficient-a practical guide to neural architecture design”, arXiv preprint arXiv:2105.12210, 2021.
- G. Philipp, D. Song, and J. G. Carbonell. “The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions”, 2018.
- M. Roodschild, J. Gotay Sardiñas, and A. Will, “A new approach for the vanishing gradient problem on sigmoid activation”, Progress in Artificial Intelligence, vol. 9, no. 4, 2020, 351–360.
- Y. Shi, Y. E. Sagduyu, and T. Erpek. “Federated learning for distributed spectrum sensing in nextg communication networks”, 2022.
- J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition”, Neural Networks, vol. 32, 2012, 323–332, https://doi.org/10.1016/j.neunet.2012.02.016, Selected Papers from IJCNN 2011.
- J. Wu, S. Drew, F. Dong, Z. Zhu, and J. Zhou. “Topology-aware federated learning in edge computing: A comprehensive survey”, 2023.
- J. Wu, S. Drew, F. Dong, Z. Zhu, and J. Zhou, “Topology-aware federated learning in edge computing: A comprehensive survey”, arXiv preprint arXiv:2302.02573, 2023.
- T. Yang, G. Andrew, H. Eichner, H. Sun, W. Li, N. Kong, D. Ramage, and F. Beaufays. “Applied federated learning: Improving google keyboard query suggestions”, 2018.
- D. Yin, Y. Chen, R. Kannan, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates”. In: J. Dy and A. Krause, eds., Proceedings of the 35th International Conference on Machine Learning, vol. 80, 2018, 5650–5659.
- M. Zhang, E. Wei, and R. Berry, “Faithful edge federated learning: Scalability and privacy”, IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, 2021, 3790–3804, 10.1109/JSAC.2021.3118423.