Workshop
Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023 (FL@FM-NeurIPS'23)
Jinghui Chen · Lixin Fan · Gauri Joshi · Sai Praneeth Karimireddy · Stacy Patterson · Shiqiang Wang · Han Yu
Hall D2 (level 1)
Sat 16 Dec, 6:25 a.m. PST
An exciting forum for researchers to exchange the recent developments in federated learning in the modern age of foundation models.
Please visit our workshop webpage for full details: https://federated-learning.org/fl@fm-neurips-2023/
Chat is not available.
Timezone: America/Los_Angeles
Schedule
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Sat 6:25 a.m. - 6:30 a.m.
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Opening remarks
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Presentation
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Sat 6:30 a.m. - 6:40 a.m.
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Text-driven Prompt Generation for Vision-Language Models in Federated Learning
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Oral
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link
SlidesLive Video |
Chen Qiu · Xingyu Li · Chaithanya Kumar Mummadi · Madan Ganesh · Zhenzhen Li · Lu Peng · Wan-Yi Lin 🔗 |
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Sat 6:40 a.m. - 6:50 a.m.
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HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning
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Oral
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link
SlidesLive Video |
Shaunak Halbe · James S Smith · Junjiao Tian · Zsolt Kira 🔗 |
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Sat 6:50 a.m. - 7:00 a.m.
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Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning
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Oral
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link
SlidesLive Video |
Jianwei Li · Sheng Liu · Qi Lei 🔗 |
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Sat 7:00 a.m. - 7:10 a.m.
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FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
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Poster
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link
SlidesLive Video |
Wenda Chu · Chulin Xie · Boxin Wang · Linyi Li · Lang Yin · Arash Nourian · Han Zhao · Bo Li 🔗 |
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Sat 7:10 a.m. - 7:35 a.m.
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Invited talk: Federated Learning by Dataset Distillation
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Oral
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SlidesLive Video |
Cho-Jui Hsieh 🔗 |
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Sat 7:35 a.m. - 8:00 a.m.
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Invited talk: Federated Learning with Public and Private Data: From Small Models to Large, and Back
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Oral
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SlidesLive Video |
Zheng Xu 🔗 |
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Sat 8:00 a.m. - 8:30 a.m.
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Break
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Sat 8:30 a.m. - 8:55 a.m.
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Invited talk: When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
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Oral
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SlidesLive Video |
Lingjuan Lyu 🔗 |
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Sat 8:55 a.m. - 9:05 a.m.
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FedSoL: Bridging Global Alignment and Local Generality in Federated Learning
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Oral
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link
SlidesLive Video |
Gihun Lee · Minchan Jeong · SangMook Kim · Jaehoon Oh · Se-Young Yun 🔗 |
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Sat 9:05 a.m. - 9:15 a.m.
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One-shot Empirical Privacy Estimation for Federated Learning
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Oral
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link
SlidesLive Video |
Galen Andrew · Peter Kairouz · Sewoong Oh · Alina Oprea · H. Brendan McMahan · Vinith Suriyakumar 🔗 |
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Sat 9:15 a.m. - 10:00 a.m.
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Panel Discussion
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Panel
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SlidesLive Video |
Sai Praneeth Karimireddy 🔗 |
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Sat 10:00 a.m. - 11:30 a.m.
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Lunch
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Sat 11:30 a.m. - 11:55 a.m.
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Invited talk: Federated Learning in Medical Imaging
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Oral
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SlidesLive Video |
Jayashree Kalpathy-Cramer 🔗 |
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Sat 11:55 a.m. - 12:20 p.m.
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Invited talk: Decentralized LLM Agent Cloud Platform
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Oral
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SlidesLive Video |
Chaoyang He 🔗 |
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Sat 12:20 p.m. - 12:30 p.m.
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Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning
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Oral
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link
SlidesLive Video |
Liam Collins · Shanshan Wu · Sewoong Oh · Khe Sim 🔗 |
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Sat 12:30 p.m. - 12:40 p.m.
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SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
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Oral
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link
SlidesLive Video |
Sara Babakniya · Ahmed Elkordy · Yahya Ezzeldin · Qingfeng Liu · Kee-Bong Song · Mostafa El-Khamy · Salman Avestimehr 🔗 |
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Sat 12:40 p.m. - 12:50 p.m.
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The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning
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Oral
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>
link
SlidesLive Video |
Justin Kang · Kannan Ramchandran · Ramtin Pedarsani 🔗 |
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Sat 12:50 p.m. - 1:00 p.m.
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Towards Building the FederatedGPT: Federated Instruction Tuning
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Oral
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>
link
SlidesLive Video |
Jianyi Zhang · Saeed Vahidian · Martin Kuo · Chunyuan Li · Ruiyi Zhang · Tong Yu · Guoyin Wang · Yiran Chen 🔗 |
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Sat 1:00 p.m. - 1:30 p.m.
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Break
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Sat 1:30 p.m. - 1:55 p.m.
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Invited talk: On the 5th Generation of Local Training Methods in Federated Learning
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Oral
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SlidesLive Video |
Peter Richtarik 🔗 |
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Sat 1:55 p.m. - 2:05 p.m.
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Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR
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Oral
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link
SlidesLive Video |
Shams Azam · Martin Pelikan · Vitaly Feldman · Kunal Talwar · Jan Silovsky · Tatiana Likhomanenko 🔗 |
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Sat 2:05 p.m. - 2:15 p.m.
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LASER: Linear Compression in Wireless Distributed Optimization
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Oral
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>
link
SlidesLive Video |
Ashok Vardhan Makkuva · Marco Bondaschi · Thijs Vogels · Martin Jaggi · Hyeji Kim · Michael Gastpar 🔗 |
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Sat 2:15 p.m. - 2:20 p.m.
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Best Paper Award Ceremony
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Announcement
)
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SlidesLive Video |
Shiqiang Wang 🔗 |
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Sat 2:20 p.m. - 3:30 p.m.
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Poster Session
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Poster
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Beyond Parameter Averaging in Model Aggregation ( Poster ) > link | Pol Garcia Recasens · Jordi Torres · Josep Lluís Berral · Søren Hauberg · Pablo Moreno-Muñoz 🔗 |
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DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization ( Poster ) > link | Liang Zhang · Kiran Thekumparampil · Sewoong Oh · Niao He 🔗 |
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An Empirical Evaluation of Federated Contextual Bandit Algorithms ( Poster ) > link | Alekh Agarwal · H. Brendan McMahan · Zheng Xu 🔗 |
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parameter averaging laws for multitask language models ( Poster ) > link | Woojin Chung · Hyowon Cho · James Thorne · Se-Young Yun 🔗 |
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Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization ( Poster ) > link | Heng Zhu · Arya Mazumdar 🔗 |
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Augmenting Federated Learning with Pretrained Transformers ( Poster ) > link | Xuechen Zhang · Mingchen Li · Xiangyu Chang · Jiasi Chen · Amit Roy-Chowdhury · Ananda Theertha Suresh · Samet Oymak 🔗 |
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Exploring User-level Gradient Inversion with a Diffusion Prior ( Poster ) > link | Zhuohang Li · Andrew Lowy · Jing Liu · Toshiaki Koike-Akino · Bradley Malin · Kieran Parsons · Ye Wang 🔗 |
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Making Batch Normalization Great in Federated Deep Learning ( Poster ) > link | Jike Zhong · Hong-You Chen · Wei-Lun (Harry) Chao 🔗 |
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Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning ( Poster ) > link | Xidong Wu · Wan-Yi Lin · Devin Willmott · Filipe Condessa · Yufei Huang · Zhenzhen Li · Madan Ganesh 🔗 |
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MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization ( Poster ) > link | Hanmin Li · Avetik Karagulyan · Peter Richtarik 🔗 |
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Private and Personalized Histogram Estimation in a Federated Setting ( Poster ) > link | Amrith Setlur · Vitaly Feldman · Kunal Talwar 🔗 |
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TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation ( Poster ) > link | Laurent Condat · Ivan Agarský · Grigory Malinovsky · Peter Richtarik 🔗 |
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FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System ( Poster ) > link | Weizhao Jin · Yuhang Yao · Shanshan Han · Carlee Joe-Wong · Srivatsan Ravi · Salman Avestimehr · Chaoyang He 🔗 |
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MOFL/D: A Federated Multi-objective Learning Framework with Decomposition ( Poster ) > link | Maria Hartmann · Grégoire Danoy · Mohammed Alswaitti · Pascal Bouvry 🔗 |
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Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning ( Poster ) > link | Georgios Papadopoulos · Yash Satsangi · Shaltiel Eloul · Marco Pistoia 🔗 |
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Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models ( Poster ) > link | Eros Fanì · Raffaello Camoriano · Barbara Caputo · Marco Ciccone 🔗 |
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Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages ( Poster ) > link | Wanru Zhao · Yihong Chen · Royson Lee · Xinchi Qiu · Yan Gao · Hongxiang Fan · Nicholas Lane 🔗 |
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Learning Optimizers for Local SGD ( Poster ) > link | Charles-Étienne Joseph · Benjamin Thérien · Abhinav Moudgil · Boris Knyazev · Eugene Belilovsky 🔗 |
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RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation ( Poster ) > link | Marco Bornstein · Amrit Bedi · Anit Kumar Sahu · Furqan Khan · Furong Huang 🔗 |
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Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models ( Poster ) > link | Yae Jee Cho · Luyang Liu · Zheng Xu · Aldi Fahrezi · Matt Barnes · Gauri Joshi 🔗 |
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FDAPT: Federated Domain-adaptive Pre-training for Language Models ( Poster ) > link | Lekang Jiang · Filip Svoboda · Nicholas Lane 🔗 |
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Backdoor Threats from Compromised Foundation Models to Federated Learning ( Poster ) > link | Xi Li · Songhe Wang · Chen Wu · Hao Zhou · Jiaqi Wang 🔗 |
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Correlated Noise Provably Beats Independent Noise for Differentially Private Learning ( Poster ) > link | Christopher A. Choquette-Choo · Krishnamurthy Dvijotham · Krishna Pillutla · Arun Ganesh · Thomas Steinke · Abhradeep Guha Thakurta 🔗 |
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User Inference Attacks on Large Language Models ( Poster ) > link | Nikhil Kandpal · Krishna Pillutla · Alina Oprea · Peter Kairouz · Christopher A. Choquette-Choo · Zheng Xu 🔗 |
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FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning ( Poster ) > link | SeongYoon Kim · Gihun Lee · Jaehoon Oh · Se-Young Yun 🔗 |
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FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis ( Poster ) > link | Connor Mclaughlin · Lili Su 🔗 |