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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
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Abstract

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Schedule

Timezone: America/Los_Angeles
6:25 AM
Opening remarks
6:30 AM
Text-driven Prompt Generation for Vision-Language Models in Federated Learning
Chen Qiu ⋅ Xingyu Li ⋅ Chaithanya Kumar Mummadi ⋅ Madan Ganesh ⋅ Zhenzhen Li ⋅ Lu Peng ⋅ Wan-Yi Lin
6:40 AM
HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning
Shaunak Halbe ⋅ James S Smith ⋅ Junjiao Tian ⋅ Zsolt Kira
7:00 AM
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
Wenda Chu ⋅ Chulin Xie ⋅ Boxin Wang ⋅ Linyi Li ⋅ Lang Yin ⋅ Arash Nourian ⋅ Han Zhao ⋅ Bo Li
8:00 AM
Break
8:55 AM
FedSoL: Bridging Global Alignment and Local Generality in Federated Learning
Gihun Lee ⋅ Minchan Jeong ⋅ SangMook Kim ⋅ Jaehoon Oh ⋅ Se-Young Yun
9:05 AM
One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew ⋅ Peter Kairouz ⋅ Sewoong Oh ⋅ Alina Oprea ⋅ H. Brendan McMahan ⋅ Vinith Suriyakumar
9:15 AM
Panel Discussion
Sai Praneeth Karimireddy
10:00 AM
Lunch
11:30 AM
Invited talk: Federated Learning in Medical Imaging
Jayashree Kalpathy-Cramer
12:20 PM
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning
Liam Collins ⋅ Shanshan Wu ⋅ Sewoong Oh ⋅ Khe Sim
12:30 PM
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
Sara Babakniya ⋅ Ahmed Elkordy ⋅ Yahya Ezzeldin ⋅ Qingfeng Liu ⋅ Kee-Bong Song ⋅ Mostafa El-Khamy ⋅ Salman Avestimehr
12:40 PM
The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning
Justin Kang ⋅ Kannan Ramchandran ⋅ Ramtin Pedarsani
12:50 PM
Towards Building the FederatedGPT: Federated Instruction Tuning
Jianyi Zhang ⋅ Saeed Vahidian ⋅ Martin Kuo ⋅ Chunyuan Li ⋅ Ruiyi Zhang ⋅ Tong Yu ⋅ Guoyin Wang ⋅ Yiran Chen
1:00 PM
Break
1:55 PM
Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR
Shams Azam ⋅ Martin Pelikan ⋅ Vitaly Feldman ⋅ Kunal Talwar ⋅ Jan Silovsky ⋅ Tatiana Likhomanenko
2:05 PM
LASER: Linear Compression in Wireless Distributed Optimization
Ashok Vardhan Makkuva ⋅ Marco Bondaschi ⋅ Thijs Vogels ⋅ Martin Jaggi ⋅ Hyeji Kim ⋅ Michael Gastpar
2:15 PM
Best Paper Award Ceremony
Shiqiang Wang
2:20 PM
Poster Session
Beyond Parameter Averaging in Model Aggregation
Pol Garcia Recasens ⋅ Jordi Torres ⋅ Josep Lluís Berral ⋅ Søren Hauberg ⋅ Pablo Moreno-Muñoz
DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization
Liang Zhang ⋅ Kiran Thekumparampil ⋅ Sewoong Oh ⋅ Niao He
An Empirical Evaluation of Federated Contextual Bandit Algorithms
Alekh Agarwal ⋅ H. Brendan McMahan ⋅ Zheng Xu
parameter averaging laws for multitask language models
Woojin Chung ⋅ Hyowon Cho ⋅ James Thorne ⋅ Se-Young Yun
Augmenting Federated Learning with Pretrained Transformers
Xuechen Zhang ⋅ Mingchen Li ⋅ Xiangyu Chang ⋅ Jiasi Chen ⋅ Amit Roy-Chowdhury ⋅ Ananda Theertha Suresh ⋅ Samet Oymak
Exploring User-level Gradient Inversion with a Diffusion Prior
Zhuohang Li ⋅ Andrew Lowy ⋅ Jing Liu ⋅ Toshiaki Koike-Akino ⋅ Bradley Malin ⋅ Kieran Parsons ⋅ Ye Wang
Making Batch Normalization Great in Federated Deep Learning
Jike Zhong ⋅ Hong-You Chen ⋅ Wei-Lun (Harry) Chao
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Xidong Wu ⋅ Wan-Yi Lin ⋅ Devin Willmott ⋅ Filipe Condessa ⋅ Yufei Huang ⋅ Zhenzhen Li ⋅ Madan Ganesh
Private and Personalized Histogram Estimation in a Federated Setting
Amrith Setlur ⋅ Vitaly Feldman ⋅ Kunal Talwar
TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation
Laurent Condat ⋅ Ivan Agarský ⋅ Grigory Malinovsky ⋅ Peter Richtarik
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Weizhao Jin ⋅ Yuhang Yao ⋅ Shanshan Han ⋅ Carlee Joe-Wong ⋅ Srivatsan Ravi ⋅ Salman Avestimehr ⋅ Chaoyang He
MOFL/D: A Federated Multi-objective Learning Framework with Decomposition
Maria Hartmann ⋅ Grégoire Danoy ⋅ Mohammed Alswaitti ⋅ Pascal Bouvry
Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning
Georgios Papadopoulos ⋅ Yash Satsangi ⋅ Shaltiel Eloul ⋅ Marco Pistoia
Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models
Eros Fanì ⋅ Raffaello Camoriano ⋅ Barbara Caputo ⋅ Marco Ciccone
Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages
Wanru Zhao ⋅ Yihong Chen ⋅ Royson Lee ⋅ Xinchi Qiu ⋅ Yan Gao ⋅ Hongxiang Fan ⋅ Nicholas Lane
Learning Optimizers for Local SGD
Charles-Étienne Joseph ⋅ Benjamin Thérien ⋅ Abhinav Moudgil ⋅ Boris Knyazev ⋅ Eugene Belilovsky
RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation
Marco Bornstein ⋅ Amrit Bedi ⋅ Anit Kumar Sahu ⋅ Furqan Khan ⋅ Furong Huang
Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models
Yae Jee Cho ⋅ Luyang Liu ⋅ Zheng Xu ⋅ Aldi Fahrezi ⋅ Matt Barnes ⋅ Gauri Joshi
FDAPT: Federated Domain-adaptive Pre-training for Language Models
Lekang Jiang ⋅ Filip Svoboda ⋅ Nicholas Lane
Backdoor Threats from Compromised Foundation Models to Federated Learning
Xi Li ⋅ Songhe Wang ⋅ Chen Wu ⋅ Hao Zhou ⋅ Jiaqi Wang
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
Christopher A. Choquette-Choo ⋅ Krishnamurthy Dvijotham ⋅ Krishna Pillutla ⋅ Arun Ganesh ⋅ Thomas Steinke ⋅ Abhradeep Guha Thakurta
User Inference Attacks on Large Language Models
Nikhil Kandpal ⋅ Krishna Pillutla ⋅ Alina Oprea ⋅ Peter Kairouz ⋅ Christopher A. Choquette-Choo ⋅ Zheng Xu
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning
SeongYoon Kim ⋅ Gihun Lee ⋅ Jaehoon Oh ⋅ Se-Young Yun