NeurIPS 2024PastLarge language modelsFederated learning

International Workshop on Federated Foundation Models in Conjunction with NeurIPS 2024

FL@FM-NeurIPS'24

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Submission deadline: Oct 17, 2024, 12:00 UTC
imported from OpenReview — check the website for extensions
Submission portal: OpenReview
Notes: Topics were auto-suggested and may be imprecise — edits welcome.

Accepted papers (25)

Fetched from OpenReview (v2) on 2026-06-10.

$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning
Filip Granqvist, Congzheng Song, Áine Cahill, Rogier van Dalen, Martin Pelikan, YI SHENG CHAN, Xiaojun Feng, Natarajan Krishnaswami, Vojta J, Mona Chitnis · PDF
Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration
Christian Internò, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer · PDF
Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning
Kai Yi, Timur Kharisov, Igor Sokolov, Peter Richtárik · PDF
Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms
Xiaochun Niu, Lili Su, Jiaming Xu, Pengkun Yang · PDF
DeComFL: Federated Learning with Dimension-Free Communication
Zhe Li, Bicheng Ying, Zidong Liu, Chaosheng Dong, Haibo Yang · PDF
Defection-Free Collaboration between Competitors in a Learning System
Mariel Werner, Sai Praneeth Karimireddy, Michael Jordan · PDF
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing
Alexander Bienstock, Antigoni Polychroniadou, Ujjwal Kumar · PDF
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
Rui Ye, Jingyi Chai, Xiangrui Liu, Yaodong Yang, Yanfeng Wang, Siheng Chen · PDF
EncCluster: Bringing Functional Encryption in Federated Foundational Models
Vasileios Tsouvalas, Samaneh Mohammadi, Ali Balador, Tanir Özçelebi, Francesco Flammini, Nirvana Meratnia · PDF
Enhancing Causal Discovery in Federated Settings with Limited Local Samples
Xianjie Guo, Liping Yi, Xiaohu Wu, Kui Yu, Gang Wang · PDF
Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees
Steffen Schotthöfer, M. Paul Laiu · PDF
Federated Learning with Generative Content
Rui Ye, Xinyu Zhu, Jingyi Chai, Lingjuan Lyu, Chen Xie, Yanfeng Wang, Siheng Chen · PDF
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator
Sunny Gupta, Amit Sethi · PDF
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
Yao Shu, Wenyang Hu, See-Kiong Ng, Bryan Kian Hsiang Low, Fei Yu · PDF
Hot Pluggable Federated Learning
Lei Shen, Zhenheng Tang, Lijun Wu, Yonggang Zhang, Xiaowen Chu, Tao Qin, Bo Han · PDF
Improving Group Connectivity for Generalization of Federated Deep Learning
Zexi Li, Jie Lin, Zhiqi Li, Didi Zhu, Rui Ye, Tao Shen, Tao Lin, Chao Wu · PDF
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models
Rui Ye, Rui Ge, Fengting Yuchi, Jingyi Chai, Yanfeng Wang, Siheng Chen · PDF
MAP: Model Merging with Amortized Pareto Front Using Limited Computation
Lu Li, Tianyu Zhang, Zhiqi Bu, Suyuchen Wang, Huan He, Jie Fu, Yonghui Wu, Jiang Bian, Yong Chen, Yoshua Bengio · PDF
Momentum Approximation in Asynchronous Private Federated Learning
Tao Yu, Congzheng Song, Jianyu Wang, Mona Chitnis · PDF
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
Muxing Wang, Pengkun Yang, Lili Su · PDF
OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning
Harish Karthikeyan, Antigoni Polychroniadou · PDF
The Future of Large Language Model Pre-training is Federated
Lorenzo Sani, Alex Iacob, Zeyu Cao, Bill Marino, Yan Gao, Tomas Paulik, Wanru Zhao, William F. Shen, Preslav Aleksandrov, Xinchi Qiu, Nicholas Donald Lane · PDF
The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain
Sergio Zaera Mata, Roberto Gómez-Espinosa Martín · PDF
Worldwide Federated Training of Language Models
Alex Iacob, Lorenzo Sani, Bill Marino, Preslav Aleksandrov, William F. Shen, Nicholas Donald Lane · PDF
ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
Wang Lu, Hao Yu, Jindong Wang, Damien Teney, Haohan Wang, Yao Zhu, Yiqiang Chen, Qiang Yang, Xing Xie, Xiangyang Ji · PDF

Accepted papers (25)

☆$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning

☆Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration

☆Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning

☆Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms

☆DeComFL: Federated Learning with Dimension-Free Communication

☆Defection-Free Collaboration between Competitors in a Learning System

☆DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing

☆Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models

☆EncCluster: Bringing Functional Encryption in Federated Foundational Models

☆Enhancing Causal Discovery in Federated Settings with Limited Local Samples

☆Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees

☆Federated Learning with Generative Content

☆FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator

☆Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models

☆Hot Pluggable Federated Learning

☆Improving Group Connectivity for Generalization of Federated Deep Learning

☆Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models

☆MAP: Model Merging with Amortized Pareto Front Using Limited Computation

☆Momentum Approximation in Asynchronous Private Federated Learning

☆On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments

☆OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning

☆The Future of Large Language Model Pre-training is Federated

☆The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain

☆Worldwide Federated Training of Language Models

☆ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning

$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning

Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration

Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning

Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms

DeComFL: Federated Learning with Dimension-Free Communication

Defection-Free Collaboration between Competitors in a Learning System

DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing

Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models

EncCluster: Bringing Functional Encryption in Federated Foundational Models

Enhancing Causal Discovery in Federated Settings with Limited Local Samples

Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees

Federated Learning with Generative Content

FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator

Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models

Hot Pluggable Federated Learning

Improving Group Connectivity for Generalization of Federated Deep Learning

Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models

MAP: Model Merging with Amortized Pareto Front Using Limited Computation

Momentum Approximation in Asynchronous Private Federated Learning

On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments

OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning

The Future of Large Language Model Pre-training is Federated

The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain

Worldwide Federated Training of Language Models

ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning