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Workshop
Mon Dec 13 05:30 AM -- 04:00 PM (PST)
New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership
Nghia Hoang · Lam Nguyen · Pin-Yu Chen · Tsui-Wei Weng · Sara Magliacane · Bryan Kian Hsiang Low · Anoop Deoras





Workshop Home Page

Federated Learning (FL) has recently emerged as the de facto framework for distributed machine learning (ML) that preserves the privacy of data, especially in the proliferation of mobile and edge devices with their increasing capacity for storage and computation. To fully utilize the vast amount of geographically distributed, diverse and privately owned data that is stored across these devices, FL provides a platform on which local devices can build their own local models whose training processes can be synchronized via sharing differential parameter updates. This was done without exposing their private training data, which helps mitigate the risk of privacy violation, in light of recent policies such as the General Data Protection Regulation (GDPR). Such potential use of FL has since then led to an explosive attention from the ML community resulting in a vast, growing amount of both theoretical and empirical literature that push FL so close to being the new standard of ML as a democratized data analytic service.

Interestingly, as FL comes closer to being deployable in real-world scenarios, it also surfaces a growing set of challenges on trustworthiness, fairness, auditability, scalability, robustness, security, privacy preservation, decentralizability, data ownership and personalizability that are all becoming increasingly important in many interrelated aspects of our digitized society. Such challenges are particularly important in economic landscapes that do not have the presence of big tech corporations with big data and are instead driven by government agencies and institutions with valuable data locked up or small-to-medium enterprises & start-ups with limited data and little funding. With this forethought, the workshop envisions the establishment of an AI ecosystem that facilitates data and model sharing between data curators as well as interested parties in the data and models while protecting personal data ownership.

Poster Session: https://gather.town/app/8bJUNHsVwXWh0K2O/nffl

Pre-workshop networking (Networking Session)
Keynote Talk by Professor Alex Pentland (Keynote Talk)
Q&A with Professor Alex Pentland (Q/A Live Session)
Contributed Talk 1: Personalized Neural Architecture Search for Federated Learning (Contributed Talk)
Contributed Talk 1 - Q/A Live session (Q/A Live session)
Contributed Talk 2: A Unified Framework to Understand Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective (Contributed Talk)
Contributed Talk 2 - Q/A Live session (Q/A Live session)
Contributed Talk 3: Architecture Personalization in Resource-constrained Federated Learning (Contributed Talk)
Contributed Talk 3 - Q/A Live Session (Q/A Live Session)
Keynote Talk by Professor Peter Richtarik (Keynote Talk)
Q&A with Professor Peter Richtarik (Q/A Live Session)
Keynote Talk by Dr. Marten van Dijk (Keynote Talk)
Q&A with Dr. Marten van Dijk (Q/A Live Session)
Lunch Break
Keynote Talk by Assistant Professor Virginia Smith (Keynote Talk)
Q&A with A/Professor Virginia Smith (Q/A Live Session)
Contributed Talk 4: Sharp Bounds for FedAvg (Local SGD) (Contributed Talk)
Contributed Talk 4 - Q/A Live Session (Q/A Live Session)
Contributed Talk 5: Efficient and Private Federated Learning with Partially Trainable Networks (Contributed Talk)
Contributed Talk 5 - Q/A Live Session (Q/A Live Session)
Contributed Talk 6: FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning (Contributed Talk)
Contributed Talk 6 - Q/A Live Session (Q/A Live Session)
Poster Session
Keynote Talk by Professor Dawn Song (Keynote Talk)
Q&A with Professor Dawn Song (Q/A Live Session)
Keynote Talk by Professor Asu Ozdaglar (Keynote Talk)
Q&A with Professor Asu Ozdaglar (Q/A Live Session)
Post-workshop Networking (Networking Session)
FairFed: Enabling Group Fairness in Federated Learning (Poster)
WAFFLE: Weighted Averaging for Personalized Federated Learning (Poster)
Sharp Bounds for FedAvg (Local SGD) (Poster)
Architecture Personalization in Resource-constrained Federated Learning (Poster)
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses (Poster)
Detecting Poisoning Nodes in Federated Learning by Ranking Gradients (Poster)
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization (Poster)
A Unified Framework to Understand Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective (Poster)
Decentralized Personalized Federated Min-Max Problems (Poster)
FedMix: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning (Poster)
Advanced Free-rider Attacks in Federated Learning (Poster)
FedJAX: Federated learning simulation with JAX (Poster)
Bayesian SignSGD Optimizer for Federated Learning (Poster)
Efficient and Private Federated Learning with Partially Trainable Networks (Poster)
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning (Poster)
Learning Federated Representations and Recommendations with Limited Negatives (Poster)
Scalable Average Consensus with Compressed Communications (Poster)
FedRAD: Federated Robust Adaptive Distillation (Poster)
Certified Robustness for Free in Differentially Private Federated Learning (Poster)
Contribution Evaluation in Federated Learning: Examining Current Approaches (Poster)
Bayesian Framework for Gradient Leakage (Poster)
Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning (Poster)
Personalized Neural Architecture Search for Federated Learning (Poster)
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach (Poster)
Secure Byzantine-Robust Distributed Learning via Clustering (Poster)
FedBABU: Towards Enhanced Representation for Federated Image Classification (Poster)
FeO2: Federated Learning with Opt-Out Differential Privacy (Poster)
Federating for Learning Group Fair Models (Poster)
Certified Federated Adversarial Training (Poster)
Iterated Vector Fields and Conservatism, with Applications to Federated Learning (Poster)
RVFR: Robust Vertical Federated Learning via Feature Subspace Recovery (Poster)
Secure Aggregation for Buffered Asynchronous Federated Learning (Poster)
Minimal Model Structure Analysis for Input Reconstruction in Federated Learning (Poster)
FedGMA: Federated Learning with Gradient Masked Averaging (Poster)
What Do We Mean by Generalization in Federated Learning? (Poster)
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer (Poster)
FedHist: A Federated-First Dataset for Learning in Healthcare (Poster)