FusionRNN: Shared Neural Parameters for Multi-Channel Distant Speech Recognition. - Titouan Parcollet; Xinchi Qiu; Nicholas D. Lane (INTERSPEECH, 2020)

Quaternion Neural Networks for Multi-Channel Distant Speech Recognition. - Xinchi Qiu; Titouan Parcollet; Mirco Ravanelli; Nicholas D. Lane; Mohamed Morchid (INTERSPEECH, 2020)

Flower: A Friendly Federated Learning Research Framework. - Daniel J. Beutel; Taner Topal; Akhil Mathur; Xinchi Qiu; Titouan Parcollet; Nicholas D. Lane (CoRR, 2020)

A first look into the carbon footprint of federated learning. - Xinchi Qiu; Titouan Parcollet; Daniel J. Beutel; Taner Topal; Akhil Mathur; Nicholas D. Lane (CoRR, 2020)

A first look into the carbon footprint of federated learning. - Xinchi Qiu; Titouan Parcollet; Javier Fernández-Marqués; Pedro Porto Buarque de Gusmão; Daniel J. Beutel; Taner Topal; Akhil Mathur; Nicholas D. Lane (CoRR, 2021)

On-device Federated Learning with Flower. - Akhil Mathur; Daniel J. Beutel; Pedro Porto Buarque de Gusmão; Javier Fernández-Marqués; Taner Topal; Xinchi Qiu; Titouan Parcollet; Yan Gao 016; Nicholas D. Lane (CoRR, 2021)

Protea: client profiling within federated systems using flower. - Wanru Zhao; Xinchi Qiu; Javier Fernández-Marqués; Pedro P. B. de Gusmao; Nicholas D. Lane (FedEdge@MobiCom, 2022)

ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. - Xinchi Qiu; Javier Fernández-Marqués; Pedro P. B. de Gusmao; Yan Gao 016; Titouan Parcollet; Nicholas Donald Lane (ICLR, 2022)

Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates. - Chenyang Ma; Xinchi Qiu; Daniel J. Beutel; Nicholas D. Lane (EuroMLSys@EuroSys, 2023)

FedL2P: Federated Learning to Personalize. - Royson Lee; Minyoung Kim 001; Da Li 001; Xinchi Qiu; Timothy M. Hospedales; Ferenc Huszar; Nicholas D. Lane (NeurIPS, 2023)

FedVal: Different good or different bad in federated learning. - Viktor Valadi; Xinchi Qiu; Pedro Porto Buarque de Gusmão; Nicholas D. Lane; Mina Alibeigi (USENIX Security Symposium, 2023)

Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates. - Chenyang Ma; Xinchi Qiu; Daniel J. Beutel; Nicholas D. Lane (CoRR, 2023)

Efficient Vertical Federated Learning with Secure Aggregation. - Xinchi Qiu; Heng Pan; Wanru Zhao; Chenyang Ma; Pedro Porto Buarque de Gusmão; Nicholas D. Lane (CoRR, 2023)

EXACT: Extensive Attack for Split Learning. - Xinchi Qiu; Ilias Leontiadis; Luca Melis; Alexandre Sablayrolles; Pierre Stock (CoRR, 2023)

vFedSec: Efficient Secure Aggregation for Vertical Federated Learning via Secure Layer. - Xinchi Qiu; Heng Pan; Wanru Zhao; Chenyang Ma; Pedro P. B. de Gusmao; Nicholas D. Lane (CoRR, 2023)

High-throughput Simulation of Federated Learning via Resource-Aware Client Placement. - Lorenzo Sani; Pedro Porto Buarque de Gusmão; Alex Iacob; Wanru Zhao; Xinchi Qiu; Yan Gao 016; Javier Fernández-Marqués; Nicholas Donald Lane (CoRR, 2023)

A First Look into the Carbon Footprint of Federated Learning. - Xinchi Qiu; Titouan Parcollet; Javier Fernández-Marqués; Pedro P. B. de Gusmao; Yan Gao 016; Daniel J. Beutel; Taner Topal; Akhil Mathur; Nicholas D. Lane (J. Mach. Learn. Res., 2023)

Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. - Wanru Zhao; Yihong Chen; Royson Lee; Xinchi Qiu; Yan Gao 016; Hongxiang Fan; Nicholas Donald Lane (ICLR, 2024)

FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation. - Tong Xia; Abhirup Ghosh; Xinchi Qiu; Cecilia Mascolo (KDD, 2024)

FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients. - Xinchi Qiu; Yan Gao 016; Lorenzo Sani; Heng Pan; Wanru Zhao; Pedro P. B. de Gusmao; Mina Alibeigi; Alex Iacob; Nicholas D. Lane (CoRR, 2024)

The Future of Large Language Model Pre-training is Federated. - Lorenzo Sani; Alex Iacob; Zeyu Cao; Bill Marino; Yan Gao 016; Tomás Paulik; Wanru Zhao; William F. Shen; Preslav Aleksandrov; Xinchi Qiu; Nicholas D. Lane (CoRR, 2024)

Sheaf HyperNetworks for Personalized Federated Learning. - Bao Nguyen; Lorenzo Sani; Xinchi Qiu; Pietro Liò; Nicholas D. Lane (CoRR, 2024)

PISTOL: Dataset Compilation Pipeline for Structural Unlearning of LLMs. - Xinchi Qiu; William F. Shen; Yihong Chen; Nicola Cancedda; Pontus Stenetorp; Nicholas D. Lane (CoRR, 2024)

Photon: Federated LLM Pre-Training. - Lorenzo Sani; Alex Iacob; Zeyu Cao; Royson Lee; Bill Marino; Yan Gao 016; Dongqi Cai 001; Zexi Li; Wanru Zhao; Xinchi Qiu; Nicholas D. Lane (CoRR, 2024)

DEPT: Decoupled Embeddings for Pre-training Language Models. - Alex Iacob; Lorenzo Sani; Meghdad Kurmanji; William F. Shen; Xinchi Qiu; Dongqi Cai 001; Yan Gao 016; Nicholas Donald Lane (ICLR, 2025)

LUNAR: LLM Unlearning via Neural Activation Redirection. - William F. Shen; Xinchi Qiu; Meghdad Kurmanji; Alex Iacob; Lorenzo Sani; Yihong Chen; Nicola Cancedda; Nicholas D. Lane (CoRR, 2025)

Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning? - Zexi Li; Xiangzhu Wang; William F. Shen; Meghdad Kurmanji; Xinchi Qiu; Dongqi Cai 001; Chao Wu; Nicholas D. Lane (CoRR, 2025)

DES-LOC: Desynced Low Communication Adaptive Optimizers for Training Foundation Models. - Alex Iacob; Lorenzo Sani; Mher Safaryan; Paris Giampouras; Samuel Horváth; Andrej Jovanovic; Meghdad Kurmanji; Preslav Aleksandrov; William F. Shen; Xinchi Qiu; Nicholas D. Lane (CoRR, 2025)

Don't Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning. - William F. Shen; Xinchi Qiu; Nicola Cancedda; Nicholas D. Lane (CoRR, 2025)

AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling. - Preslav Aleksandrov; Meghdad Kurmanji; Fernando García-Redondo; David O'Shea; William F. Shen; Alex Iacob; Lorenzo Sani; Xinchi Qiu; Nicola Cancedda; Nicholas D. Lane (CoRR, 2025)