Workshop
Federated Learning: Recent Advances and New Challenges
Shiqiang Wang · Nathalie Baracaldo · Olivia Choudhury · Gauri Joshi · Peter Richtarik · Praneeth Vepakomma · Han Yu
Room 298 - 299
Fri 2 Dec, 6:30 a.m. PST
Training machine learning models in a centralized fashion often faces significant challenges due to regulatory and privacy concerns in real-world use cases. These include distributed training data, computational resources to create and maintain a central data repository, and regulatory guidelines (GDPR, HIPAA) that restrict sharing sensitive data. Federated learning (FL) is a new paradigm in machine learning that can mitigate these challenges by training a global model using distributed data, without the need for data sharing. The extensive application of machine learning to analyze and draw insight from real-world, distributed, and sensitive data necessitates familiarization with and adoption of this relevant and timely topic among the scientific community.
Despite the advantages of FL, and its successful application in certain industry-based cases, this field is still in its infancy due to new challenges that are imposed by limited visibility of the training data, potential lack of trust among participants training a single model, potential privacy inferences, and in some cases, limited or unreliable connectivity.
The goal of this workshop is to bring together researchers and practitioners interested in FL. This day-long event will facilitate interaction among students, scholars, and industry professionals from around the world to understand the topic, identify technical challenges, and discuss potential solutions. This will lead to an overall advancement of FL and its impact in the community, while noting that FL has become an increasingly popular topic in the machine learning community in recent years.
Schedule
Fri 6:30 a.m. - 6:35 a.m.
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Opening Remarks
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Opening Remarks
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Shiqiang Wang 🔗 |
Fri 6:35 a.m. - 6:53 a.m.
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Trustworthy Federated Learning
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Invited Talk
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SlidesLive Video |
Bo Li 🔗 |
Fri 6:53 a.m. - 6:57 a.m.
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Trustworthy Federated Learning - Q&A
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Q&A
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Fri 6:57 a.m. - 7:15 a.m.
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Asynchronous Optimization: Delays, Stability, and the Impact of Data Heterogeneity
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Invited Talk
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SlidesLive Video |
Konstantin Mishchenko 🔗 |
Fri 7:15 a.m. - 7:19 a.m.
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Asynchronous Optimization: Delays, Stability, and the Impact of Data Heterogeneity - Q&A
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Q&A
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Fri 7:20 a.m. - 7:27 a.m.
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Conditional Moment Alignment for Improved Generalization in Federated Learning
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Oral
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SlidesLive Video |
Jayanth Reddy Regatti · Songtao Lu · Abhishek Gupta · Ness Shroff 🔗 |
Fri 7:30 a.m. - 7:37 a.m.
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Mechanisms that Incentivize Data Sharing in Federated Learning
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Oral
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SlidesLive Video |
Sai Praneeth Karimireddy · Wenshuo Guo · Michael Jordan 🔗 |
Fri 7:40 a.m. - 7:47 a.m.
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Federated Learning with Online Adaptive Heterogeneous Local Models
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Oral
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SlidesLive Video |
Hanhan Zhou · Tian Lan · Guru Prasadh Venkataramani · Wenbo Ding 🔗 |
Fri 7:50 a.m. - 7:57 a.m.
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LightVeriFL: Lightweight and Verifiable Secure Federated Learning
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Oral
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SlidesLive Video |
Baturalp Buyukates · Jinhyun So · Hessam Mahdavifar · Salman Avestimehr 🔗 |
Fri 8:00 a.m. - 8:30 a.m.
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Break
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Fri 8:30 a.m. - 8:37 a.m.
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Efficient Federated Random Subnetwork Training
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Oral
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SlidesLive Video |
Francesco Pase · Berivan Isik · Deniz Gunduz · Tsachy Weissman · Michele Zorzi 🔗 |
Fri 8:40 a.m. - 8:47 a.m.
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Group privacy for personalized federated learning
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Oral
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SlidesLive Video |
Filippo Galli · Sayan Biswas · Gangsoo Zeong · Tommaso Cucinotta · Catuscia Palamidessi 🔗 |
Fri 8:50 a.m. - 8:57 a.m.
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To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning
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Oral
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SlidesLive Video |
Yae Jee Cho · Divyansh Jhunjhunwala · Tian Li · Virginia Smith · Gauri Joshi 🔗 |
Fri 9:00 a.m. - 9:07 a.m.
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SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
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Oral
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SlidesLive Video |
Marco Bornstein · Tahseen Rabbani · Evan Wang · Amrit Bedi · Furong Huang 🔗 |
Fri 9:10 a.m. - 9:15 a.m.
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Best Paper Announcement
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Other
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SlidesLive Video |
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Fri 9:15 a.m. - 10:00 a.m.
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Poster Session 1
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Poster Session
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Fri 10:00 a.m. - 11:30 a.m.
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Lunch Break
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Fri 11:30 a.m. - 11:37 a.m.
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FL Games: A Federated Learning Framework for Distribution Shifts
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Oral
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SlidesLive Video |
Sharut Gupta · Kartik Ahuja · Mohammad Havaei · Niladri Chatterjee · Yoshua Bengio 🔗 |
Fri 11:40 a.m. - 11:47 a.m.
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Verifiable Federated Machine Learning
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Oral
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SlidesLive Video |
Simone Bottoni · Giulio Zizzo · Stefano Braghin · Alberto Trombetta 🔗 |
Fri 11:50 a.m. - 11:57 a.m.
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Accelerated Federated Optimization with Quantization
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Oral
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SlidesLive Video |
Yeojoon Youn · Bhuvesh Kumar · Jacob Abernethy 🔗 |
Fri 12:00 p.m. - 12:07 p.m.
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Tackling Personalized Federated Learning with Label Concept Drift via Hierarchical Bayesian Modeling
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Oral
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SlidesLive Video |
Xingchen Ma · Junyi Zhu · Matthew Blaschko 🔗 |
Fri 12:10 p.m. - 1:00 p.m.
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Panel
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Panel
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SlidesLive Video |
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Fri 1:00 p.m. - 1:30 p.m.
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Break
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Fri 1:30 p.m. - 1:48 p.m.
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On the Unreasonable Effectiveness of Federated Averaging with Heterogenous Data
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Invited Talk
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SlidesLive Video |
Jianyu Wang 🔗 |
Fri 1:48 p.m. - 1:52 p.m.
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On the Unreasonable Effectiveness of Federated Averaging with Heterogenous Data - Q&A
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Q&A
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Fri 1:52 p.m. - 2:10 p.m.
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Scalable and Communication-Efficient Vertical Federated Learning
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Invited Talk
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SlidesLive Video |
Stacy Patterson 🔗 |
Fri 2:10 p.m. - 2:14 p.m.
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Scalable and Communication-Efficient Vertical Federated Learning - Q&A
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Q&A
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Fri 2:15 p.m. - 3:00 p.m.
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Poster Session 2
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Poster Session
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FedTH : Tree-based Hierarchical Image Classification in Federated Learning ( Poster ) > link | Jaeheon Kim · Bong Jun Choi 🔗 |
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Unbounded Gradients in Federated Leaning with Buffered Asynchronous Aggregation ( Poster ) > link | M. Taha Toghani · Cesar Uribe 🔗 |
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Early Detection of Sexual Predators with Federated Learning ( Poster ) > link | Khaoula Chehbouni · Gilles Caporossi · Reihaneh Rabbany · Martine De Cock · Golnoosh Farnadi 🔗 |
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Self-Supervised Vertical Federated Learning ( Poster ) > link | Timothy Castiglia · Shiqiang Wang · Stacy Patterson 🔗 |
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On the Vulnerability of Backdoor Defenses for Federated Learning ( Poster ) > link | Pei Fang · Jinghui Chen 🔗 |
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Towards Provably Personalized Federated Learning via Threshold-Clustering of Similar Clients ( Poster ) > link | Mariel A Werner · Lie He · Sai Praneeth Karimireddy · Michael Jordan · Martin Jaggi 🔗 |
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Building Large Machine Learning Models from Small Distributed Models: A Layer Matching Approach ( Poster ) > link | xinwei zhang · Bingqing Song · Mehrdad Honarkhah · Jie Ding · Mingyi Hong 🔗 |
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VOTING-BASED APPROACHES FOR DIFFERENTIALLY PRIVATE FEDERATED LEARNING ( Poster ) > link | Yuqing Zhu · Xiang Yu · Yi-Hsuan Tsai · Francesco Pittaluga · Masoud Faraki · Manmohan Chandraker · Yu-Xiang Wang 🔗 |
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DASH: Decentralized CASH for Federated Learning ( Poster ) > link | Md Ibrahim Ibne Alam · Koushik Kar · Theodoros Salonidis · Horst Samulowitz 🔗 |
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Accelerating Adaptive Federated Optimization with Local Gossip Communications ( Poster ) > link | Yujia Wang · Pei Fang · Jinghui Chen 🔗 |
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Federated Progressive Sparsification (Purge-Merge-Tune)+ ( Poster ) > link | Dimitris Stripelis · Umang Gupta · Greg Ver Steeg · Jose-Luis Ambite 🔗 |
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A Multi-Token Coordinate Descent Method for Vertical Federated Learning ( Poster ) > link | Pedro Valdeira · Yuejie Chi · Claudia Soares · Joao Xavier 🔗 |
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ColRel: Collaborative Relaying for Federated Learning over Intermittently Connected Networks ( Poster ) > link | Rajarshi Saha · Michal Yemini · Emre Ozfatura · Deniz Gunduz · Andrea Goldsmith 🔗 |
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Understanding Federated Learning through Loss Landscape Visualizations: A Pilot Study ( Poster ) > link | Ziwei Li · Hong-You Chen · Han Wei Shen · Wei-Lun Chao 🔗 |
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Differentially Private Federated Quantiles with the Distributed Discrete Gaussian Mechanism ( Poster ) > link | Krishna Pillutla · Yassine Laguel · Jérôme Malick · Zaid Harchaoui 🔗 |
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Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout ( Poster ) > link | Chen Dun · Mirian Hipolito Garcia · Dimitrios Dimitriadis · Christopher Jermaine · Anastasios Kyrillidis 🔗 |
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FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings ( Poster ) > link | Junyi Li · Heng Huang 🔗 |
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Privacy-Preserving Data Filtering in Federated Learning Using Influence Approximation ( Poster ) > link | Ljubomir Rokvic · Panayiotis Danassis · Boi Faltings 🔗 |
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Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning ( Poster ) > link | John Nguyen · Jianyu Wang · Kshitiz Malik · Maziar Sanjabi · Mike Rabbat 🔗 |
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Trusted Aggregation (TAG): Model Filtering Backdoor Defense In Federated Learning ( Poster ) > link | Joseph Lavond · Minhao Cheng · Yao Li 🔗 |
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Cross-device Federated Architecture Search ( Poster ) > link | Stefanos Laskaridis · Javier Fernandez-Marques · Łukasz Dudziak 🔗 |
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Client-Private Secure Aggregation for Privacy-Preserving Federated Learning ( Poster ) > link | Parker Newton · Olivia Choudhury · Bill Horne · Vidya Ravipati · Divya Bhargavi · Ujjwal Ratan 🔗 |
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Motley: Benchmarking Heterogeneity and Personalization in Federated Learning ( Poster ) > link | Shanshan Wu · Tian Li · Zachary Charles · Yu Xiao · Ken Liu · Zheng Xu · Virginia Smith 🔗 |
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Federated Learning for Predicting the Next Node in Action Flows ( Poster ) > link | Daniel Lopes · João Nadkarni · Filipe Assunção · Miguel Lopes · Luís Rodrigues 🔗 |
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The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning ( Poster ) > link | Chuan Guo · Kamalika Chaudhuri · Pierre STOCK · Mike Rabbat 🔗 |
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Find Your Friends: Personalized Federated Learning with the Right Collaborators ( Poster ) > link | Yi Sui · Junfeng Wen · Yenson Lau · Brendan Ross · Jesse Cresswell 🔗 |
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Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks ( Poster ) > link | Saeed Vahidian · Mahdi Morafah · Chen Chen · Mubarak Shah · Bill Lin 🔗 |
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Federated Fairness without Access to Demographics ( Poster ) > link | Afroditi Papadaki · Natalia Martinez · Martin Bertran · Guillermo Sapiro · Miguel Rodrigues 🔗 |
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FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations ( Poster ) > link | Mirian Hipolito Garcia · Andre Manoel · Daniel Madrigal · Robert Sim · Dimitrios Dimitriadis 🔗 |
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Decentralized Learning with Random Walks and Communication-Efficient Adaptive Optimization ( Poster ) > link | Aleksei Triastcyn · Matthias Reisser · Christos Louizos 🔗 |
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Accelerating Federated Learning Through Attention on Local Model Updates ( Poster ) > link | Parsa Assadi · Byung Hoon Ahn · Hadi Esmaeilzadeh 🔗 |
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Federated Frank-Wolfe Algorithm ( Poster ) > link | Ali Dadras · Karthik Prakhya · Alp Yurtsever 🔗 |
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How to Combine Variational Bayesian Networks in Federated Learning ( Poster ) > link | Atahan Özer · Kadir Burak Buldu · Abdullah Akgül · Gozde Unal 🔗 |
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Stochastic Gradient Methods with Compressed Communication for Decentralized Saddle Point Problems ( Poster ) > link | Chhavi Sharma · Vishnu Narayanan · Balamurugan Palaniappan 🔗 |
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Refined Convergence and Topology Learning for Decentralized Optimization with Heterogeneous Data ( Poster ) > link | Batiste Le bars · Aurélien Bellet · Marc Tommasi · Erick Lavoie · Anne-marie Kermarrec 🔗 |
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AIMHI: Protecting Sensitive Data through Federated Co-Training ( Poster ) > link | Amr Abourayya · Michael Kamp · Erman Ayday · Jens Kleesiek · Kanishka Rao · Geoffrey Webb · Bharat Rao 🔗 |
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A Novel Model-Based Attribute Inference Attack in Federated Learning ( Poster ) > link | ilias driouich · CHUAN XU · Giovanni Neglia · Frederic Giroire · Eoin Thomas 🔗 |
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FedSHIBU: Federated Similarity-based Head Independent Body Update ( Poster ) > link | Athul Sreemathy Raj · Irene Tenison · Kacem Khaled · Felipe de Magalhães · Athul Sreemathy Raj 🔗 |
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Revisiting the Activation Function for Federated Image Classification ( Poster ) > link | Jaewoo Shin · Taehyeon Kim · Se-Young Yun 🔗 |
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Adaptive Sparse Federated Learning in Large Output Spaces via Hashing ( Poster ) > link | Zhaozhuo Xu · Luyang Liu · Zheng Xu · Anshumali Shrivastava 🔗 |
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FLARE: Federated Learning from Simulation to Real-World ( Poster ) > link |
23 presentersHolger Roth · Yan Cheng · Yuhong Wen · Te-Chung (Isaac) Yang · Ziyue Xu · Yuan-Ting Hsieh · Kristopher Kersten · Ahmed Harouni · Can Zhao · Kevin Lu · Zhihong Zhang · Wenqi Li · Andriy Myronenko · Dong Yang · Sean Yang · Nicola Rieke · Abood Quraini · Chester Chen · Daguang Xu · Nic Ma · Prerna Dogra · Mona Flores · Andrew Feng |
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PerFedSI: A Framework for Personalized Federated Learning with Side Information ( Poster ) > link | Liam Collins · Enmao Diao · Tanya Roosta · Jie Ding · Tao Zhang 🔗 |
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FedSynth: Gradient Compression via Synthetic Data in Federated Learning ( Poster ) > link | Shengyuan Hu · Jack Goetz · Kshitiz Malik · Hongyuan Zhan · Zhe Liu · Yue Liu 🔗 |
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Personalized Multi-tier Federated Learning ( Poster ) > link | Sourasekhar Banerjee · Alp Yurtsever · Monowar Bhuyan 🔗 |
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FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution ( Poster ) > link | Saeed Vahidian · Mahdi Morafah · Weijia Wang · Bill Lin 🔗 |
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Private and Robust Federated Learning using Private Information Retrieval and Norm Bounding ( Poster ) > link | Hamid Mozaffari · Virendra Marathe · Dave Dice 🔗 |
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Reconciling Security and Communication Efficiency in Federated Learning ( Poster ) > link | Karthik Prasad · Sayan Ghosh · Graham Cormode · Ilya Mironov · Ashkan Yousefpour · Pierre STOCK 🔗 |
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FedToken: Tokenized Incentives for Data Contribution in Federated Learning ( Poster ) > link | Shashi Raj Pandey · Lam Nguyen · Petar Popovski 🔗 |
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Federated Continual Learning with Differentially Private Data Sharing ( Poster ) > link | Giulio Zizzo · Ambrish Rawat · Naoise Holohan · Seshu Tirupathi 🔗 |
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$z$-SignFedAvg: A unified sign-based stochastic compression for federated learning ( Poster ) > link | Zhiwei Tang · Yanmeng Wang · Tsung-Hui Chang 🔗 |
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Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information ( Poster ) > link | Kiwan Maeng · Chuan Guo · Sanjay Kariyappa · G. Edward Suh 🔗 |
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FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning ( Poster ) > link | Yuanhao Xiong · Ruochen Wang · Minhao Cheng · Felix Yu · Cho-Jui Hsieh 🔗 |
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With a Little Help from My Friend: Server-Aided Federated Learning with Partial Client Participation ( Poster ) > link | Haibo Yang · Peiwen Qiu · Prashant Khanduri · Jia Liu 🔗 |
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Federated Learning of Large Models at the Edge via Principal Sub-Model Training ( Poster ) > link | Yue Niu · Saurav Prakash · Souvik Kundu · Sunwoo Lee · Salman Avestimehr 🔗 |
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FedRule: Federated Rule Recommendation System with Graph Neural Networks ( Poster ) > link | Yuhang Yao · Mohammad Mahdi Kamani · Zhongwei Cheng · Lin Chen · Carlee Joe-Wong · Tianqiang Liu 🔗 |
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Improving Vertical Federated Learning by Efficient Communication with ADMM ( Poster ) > link | Chulin Xie · Pin-Yu Chen · Ce Zhang · Bo Li 🔗 |
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Subject Level Differential Privacy with Hierarchical Gradient Averaging ( Poster ) > link | Virendra Marathe · Pallika Kanani · Daniel Peterson 🔗 |
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MocoSFL: enabling cross-client collaborative self-supervised learning ( Poster ) > link | Jingtao Li · Lingjuan Lyu · Daisuke Iso · Chaitali Chakrabarti · Michael Spranger 🔗 |
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Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing ( Poster ) > link | Marco Schreyer · Hamed Hemati · Damian Borth · Miklos Vasarhelyi 🔗 |
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Certified Robustness in Federated Learning ( Poster ) > link | Motasem Alfarra · Juan Perez · Egor Shulgin · Peter Richtarik · Bernard Ghanem 🔗 |
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Federated Sparse Training: Lottery Aware Model Compression for Resource Constrained Edge ( Poster ) > link | Sara Babakniya · Souvik Kundu · Saurav Prakash · Yue Niu · Salman Avestimehr 🔗 |
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Asynchronous speedup in decentralized optimization ( Poster ) > link | Mathieu Even · Hadrien Hendrikx · Laurent Massoulié 🔗 |