Published in arXiv, 2025
We present LUNAR, a novel unlearning methodology grounded in the Linear Representation Hypothesis.
Recommended citation: William F. Shen, Xinchi Qiu, Meghdad Kurmanji, Alex Iacob, Lorenzo Sani, Yihong Chen, Nicola Cancedda, & Nicholas D. Lane. (2025). LUNAR: LLM Unlearning via Neural Activation Redirection.
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Published in MLSys 2025 - The Eighth Annual Conference on Machine Learning and Systems, 2024
We present Photon, the first fully federated system for the federated pre-training of large language models.
Recommended citation: Lorenzo Sani, Alex Iacob, Zeyu Cao, Royson Lee, Bill Marino, Yan Gao, Dongqi Cai, Zexi Li, Wanru Zhao, Xinchi Qiu, & Nicholas D. Lane. (2024). Photon: Federated LLM Pre-Training.
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Published in ICLR 2025 - The Thirteenth International Conference on Learning Representations, 2024
This work proposes a novel approach to sparse federated training that does not require to fix a global mask to be efficient.
Recommended citation: Adriano Guastella*, Lorenzo Sani*, Alex Iacob, Alessio Mora, Paolo Bellavista, & Nicholas Donald Lane (2025). SparsyFed: Sparse Adaptive Federated Learning. In The Thirteenth International Conference on Learning Representations.
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Published in ICLR 2025 - The Thirteenth International Conference on Learning Representations, 2024
This work proposes a novel pre-training method for language models that decouples the embeddings from the rest of the model.
Recommended citation: Alex Iacob*, Lorenzo Sani*, Meghdad Kurmanji, William F. Shen, Xinchi Qiu, Dongqi Cai, Yan Gao, & Nicholas Donald Lane (2025). DEPT: Decoupled Embeddings for Pre-training Language Models. In The Thirteenth International Conference on Learning Representations.
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Published in arXiv, 2024
Sheaf HyperNetworks is a novel HyperNetwork-based methodology to improve the performance of any personalized federated learning setting.
Recommended citation: Bao Nguyen, Lorenzo Sani, Xinchi Qiu, Pietro Liò, & Nicholas D. Lane. (2024). Sheaf HyperNetworks for Personalized Federated Learning.
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Published in arXiv, 2024
FedAnchor is a methodology to solve federated semi-supervised learning problems.
Recommended citation: Xinchi Qiu, Yan Gao, Lorenzo Sani, Heng Pan, Wanru Zhao, Pedro P. B. Gusmao, Mina Alibeigi, Alex Iacob, & Nicholas D. Lane. (2024). FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients.
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Published in arXiv, 2023
This paper is about Pollen, a system that enables high-throughput simulation of federated learning.
Recommended citation: Lorenzo Sani, Pedro Porto Buarque de Gusmão, Alex Iacob, Wanru Zhao, Xinchi Qiu, Yan Gao, Javier Fernandez-Marques, & Nicholas Donald Lane. (2023). High-throughput Simulation of Federated Learning via Resource-Aware Client Placement.
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Published in arXiv, 2022
This paper is about Flower, the federated learning framework developed at the University of Cambridge by the MLSys group.
Recommended citation: Beutel, D., Topal, T., Mathur, A., Qiu, X., Fernandez-Marques, J., Gao, Y., Sani, L., Kwing, H., Parcollet, T., Gusmão, P., & Lane, N. (2020). Flower: A Friendly Federated Learning Research Framework. arXiv preprint arXiv:2007.14390.
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