References
- McMahan B, Moore E, Ramage D, Hampson S, Agüera y Arcas B. Communication-efficient learning of deep networks from decentralized data. In Proceedings of the International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research.
PMLR ;2017 . pp. 1273–1282. - Ma X, Zhu J, Lin Z, Chen S, Qin Y. A state-of-the-art survey on solving non-iid data in federated learning. Future Gener. Comput. Syst. 2022;135:244–258. DOI: 10.1016/j.future.2022.05.003
- Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, Zhang Y.
Personalized cross-silo federated learning on non-iid data . In: EAAI 2021. AAAI Press; 2021. pp. 7865–7873. DOI: 10.1609/aaai.v35i9.16960 - Karimireddy SP, Kale S, Mohri M, Reddi SJ, Stich SU, Suresh AT. SCAFFOLD: stochastic controlled averaging for federated learning. In: ICML 2020, volume 119 of Proceedings of Machine Learning Research.
PMLR ;2020 . pp. 5132–5143. - Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V. Federated optimization in heterogeneous networks. In: Proceedings of the Third Conference on Machine Learning and Systems. Austin, TX, USA:
MLSys ,2020 .mlsys.org - Chen X, Xiao C, Liu Y. Confusion-resistant federated learning via diffusion-based data harmonization on non-iid data. In: Globerson A, Mackey L, Belgrave D, Fan A, Paquet U, Tomczak JM, Zhang C, editors. Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024. Vancouver, BC, Canada:
NeurIPS ;2024 . DOI: 10.52202/079017-4368 - Sattler F, Wiedemann S, Müller KR, Samek W. Robust and communication-efficient federated learning from non-i.i.d. data. IEEE Trans. Neural Networks Learn. Syst. 2020;31(9):3400–3413. DOI: 10.1109/TNNLS.2019.2944481
- Domini D, Farabegoli N, Aguzzi G, Viroli M, Esterke L. Decentralized proximity-aware clustering for collective self-federated learning. Internet of Things 2026;35:
101841 . DOI: 10.1016/j.iot.2025.101841 - Domini D.
Towards self-adaptive cooperative learning in collective systems . In: ACSOS 2024 – Companion. Aarhus, Denmark, September 16–20, 2024. IEEE; 2024. pp. 158–160. DOI: 10.1109/ACSOS-C63493.2024.00049 - Esterle L.
Deep learning in multiagent systems . In: Deep Learning for Robot Perception and Cognition. Elsevier; 2022. pp. 435–460. DOI: 10.1016/B978-0-32-385787-1.00022-1 - Malucelli N, Domini D, Aguzzi G, Viroli M. Neighbor-based decentralized training strategies for multi-agent reinforcement learning. In: Hong J, Battiato S, Esposito C, Park JW, Przybylek A, editors. Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, SAC 2025, Catania International Airport. Catania, Italy,
31 March 2025 – 4 April 2025 .ACM ; 2025. pp. 1250–1257. DOI: 10.1145/3672608.3707923 - Ghosh A, Chung J, Yin D, Ramchandran K. An efficient framework for clustered federated learning. IEEE Trans. Inf. Theory 2022;68(12):8076–8091. DOI: 10.1109/TIT.2022.3192506
- Domini D, Aguzzi G, Farabegoli N, Viroli M, Esterle L. Proximity-based self-federated learning. In: ACSOS 2024. Aarhus, Denmark:
September 16–20, 2024 .IEEE ; 2024. pp. 139–144. DOI: 10.1109/ACSOS61780.2024.00033 - Li X, Chen X, Tang B, Wang S, Xuan Y, Zhao Z. Unsupervised graph structure-assisted personalized federated learning. In: Proceedings of the European Conference on Artificial Intelligence, volume 372 of Frontiers in Artificial Intelligence and Applications.
IOS Press ;2023 . pp. 1430–1438. DOI: 10.3233/FAIA230421 - Li Q, Diao Y, Chen Q, He B. Federated learning on non-iid data silos: An experimental study. In: Proceedings of the International Conference on Data Engineering.
IEEE ;2022 . pp. 965–978. DOI: 10.1109/ICDE53745.2022.00077 - Huang W, Ye M, Shi Z, Wan G, Li H, Du B, Yang Q. Federated learning for generalization, robustness, fairness: A survey and benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 2024;46(12):9387–9406. DOI: 10.1109/TPAMI.2024.3418862
- Ansel J, et al.
Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation . In: ASPLOS. ACM; 2024. pp. 929–947. DOI: 10.1145/3620665.3640366 - TorchVision maintainers and contributors. TorchVision: PyTorch’s Computer Vision library, November 2016.
- LeCun Y, Cortes C, Burges C, et al. Mnist handwritten digit database, 2010.
- Krizhevsky A, Nair V, Hinton G. Cifar-10 (Canadian institute for advanced research).
- Krizhevsky A, Nair V, Hinton G. Cifar-100 (Canadian institute for advanced research).
- Zhang Z, Song Y, Qi H. Age progression/regression by conditional adversarial autoencoder. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Honolulu, HI, USA,
July 21–26, 2017 .IEEE Computer Society ; 2017. pp. 4352–4360. DOI: 10.1109/CVPR.2017.463 - Lin T, Kong L, Stich SU, Jaggi M. Ensemble distillation for robust model fusion in federated learning. In: NeurIPS 2020; 2020.
- Wang J, Liu Q, Liang H, Joshi G, Poor HV. Tackling the objective inconsistency problem in heterogeneous federated optimization. In: NeurIPS 2020; 2020.
- Domini D, Erhan L, Aguzzi G, Cavallaro L, Zenoozi AD, Liotta A, Viroli M. Sparse self-federated learning for energy efficient cooperative intelligence in society 5.0. CoRR, abs/2507.07613; 2025. DOI: 10.1109/IJCNN64981.2025.11228400
- Domini D, Aguzzi G, Esterle L, Viroli M. FBFL: A field-based coordination approach for data heterogeneity in federated learning. CoRR, abs/2502.08577; 2025.
- He C, et al. Fedml: A research library and benchmark for federated machine learning. CoRR, abs/2007.13518; 2020.
- Beutel DJ, Topal T, Mathur A, Qiu X, Parcollet T, Lane ND. Flower: A friendly federated learning research framework. CoRR, abs/2007.14390; 2020.
- Lai F, Dai Y, Singapuram SSV, Liu J, Zhu X, Madhyastha HV, Chowdhury M. Fedscale: Benchmarking model and system performance of federated learning at scale. In: ICML 2022, volume 162 of Proceedings of Machine Learning Research.
PMLR ;2022 . pp. 11814–11827. - Caldas S, Wu P, Li T, Konečn’y J, McMahan HB, Smith V, Talwalkar A. LEAF: A benchmark for federated settings. CoRR, abs/1812.01097; 2018.
- Elvebakken MF, Iosifidis A, Esterle L. Adaptive parameterization of deep learning models for federated learning. CoRR, abs/2302.02949; 2023.
- Kingma DP, Ba J. Adam: A method for stochastic optimization. CoRR, abs/1412.6980; 2014.
- Cohen G, Afshar S, Tapson J, van Schaik A. EMNIST: an extension of MNIST to handwritten letters. CoRR, abs/1702.05373; 2017. DOI: 10.1109/IJCNN.2017.7966217
- Domini D, Aguzzi G, Esterle L, Viroli M.
Field-based coordination for federated learning . In: COORDINATION 2024, volume 14676 of Lecture Notes in Computer Science. Springer; 2024. pp. 56–74. DOI: 10.1007/978-3-031-62697-5_4
