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 ICML community in recent years.
| Opening Remarks | |
| Algorithms for Efficient Federated and Decentralized Learning (Invited Talk) | |
| Algorithms for Efficient Federated and Decentralized Learning (Q&A) (Q&A) | |
| Contributed Oral Presentation Session 1 (Live Presentations) | |
| The ML data center is dead: What comes next? (Invited Talk) | |
| The ML data center is dead: What comes next? (Q&A) (Q&A) | |
| Break | |
| Contributed Oral Presentation Session 2 (Live Presentations) | |
| Pandemic Response with Crowdsourced Data: The Participatory Privacy Preserving Approach (Invited Talk) | |
| Pandemic Response with Crowdsourced Data: The Participatory Privacy Preserving Approach (Q&A) (Q&A) | |
| Industrial Panel (Panel) | |
| Poster Session 1 & Industrial Booths (GatherTown Session) | |
| Break | |
| Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing (Invited Talk) | |
| Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing (Q&A) (Q&A) | |
| Contributed Oral Presentation Session 3 (Live Presentations) | |
| Optimization Aspects of Personalized Federated Learning (Invited Talk) | |
| Optimization Aspects of Personalized Federated Learning (Q&A) (Q&A) | |
| Poster Session 2 & Industrial Booths (GatherTown Session) | |
| Break | |
| Dreaming of Federated Robustness: Inherent Barriers and Unavoidable Tradeoffs (Invited Talk) | |
| Dreaming of Federated Robustness: Inherent Barriers and Unavoidable Tradeoffs (Q&A) (Q&A) | |
| Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning (Invited Talk) | |
| Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning (Q&A) (Q&A) | |
| Closing Remarks | |
| Subgraph Federated Learning with Missing Neighbor Generation (Workshop Poster) | |
| A New Analysis Framework for Federated Learning on Time-Evolving Heterogeneous Data (Workshop Poster) | |
| Achieving Optimal Sample and Communication Complexities for Non-IID Federated Learning (Workshop Poster) | |
| FedMix: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning (Workshop Poster) | |
| Federated Learning with Metric Loss (Workshop Poster) | |
| Federated Random Reshuffling with Compression and Variance Reduction (Workshop Poster) | |
| Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity (Workshop Poster) | |
| OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning (Workshop Poster) | |
| FedNL: Making Newton-Type Methods Applicable to Federated Learning (Workshop Poster) | |
| Smoothness-Aware Quantization Techniques (Workshop Poster) | |
| MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization (Workshop Poster) | |
| FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout (Workshop Poster) | |
| FlyNN: Fruit-fly Inspired Federated Nearest Neighbor Classification (Workshop Poster) | |
| Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning (Workshop Poster) | |
| Defending against Reconstruction Attack in Vertical Federated Learning (Workshop Poster) | |
| SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks (Workshop Poster) | |
| Towards Federated Learning With Byzantine-Robust Client Weighting (Workshop Poster) | |
| Handling Both Stragglers and Adversaries for Robust Federated Learning (Workshop Poster) | |
| Industrial Booth (IBM) (Workshop Poster) | |
| GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization (Workshop Poster) | |
| Decentralized federated learning of deep neural networks on non-iid data (Workshop Poster) | |
| Implicit Gradient Alignment in Distributed and Federated Learning (Workshop Poster) | |
| Accelerating Federated Learning with Split Learning on Locally Generated Losses (Workshop Poster) | |
| - (Workshop Poster) | |
| Industrial Booth (Google) (Workshop Poster) | |
| Industrial Booth (Facebook) (Workshop Poster) | |
| Federated Learning with Buffered Asynchronous Aggregation (Workshop Poster) | |
| Optimal Model Averaging: Towards Personalized Collaborative Learning (Workshop Poster) | |
| EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback (Workshop Poster) | |
| Federated Multi-Task Learning under a Mixture of Distributions (Workshop Poster) | |
| Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization over Time-Varying Networks (Workshop Poster) | |
| Multistage stepsize schedule in Federated Learning: Bridging Theory and Practice (Workshop Poster) | |
| A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning (Workshop Poster) | |
| Robust and Differentially Private Mean Estimation (Workshop Poster) | |
| FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation (Workshop Poster) | |
| On Large-Cohort Training for Federated Learning (Workshop Poster) | |
| Gradient Inversion with Generative Image Prior (Workshop Poster) | |
| BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning (Workshop Poster) | |
| Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding (Workshop Poster) | |
| Bi-directional Adaptive Communication for Heterogenous Distributed Learning (Workshop Poster) | |
| BYGARS: Byzantine SGD with Arbitrary Number of Attackers Using Reputation Scores (Workshop Poster) | |
| Diverse Client Selection for Federated Learning: Submodularity and Convergence Analysis (Workshop Poster) | |
| New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning (Workshop Poster) | |
| Local Adaptivity in Federated Learning: Convergence and Consistency (Workshop Poster) | |
| Federated Graph Classification over Non-IID Graphs (Workshop Poster) | |
| Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy (Workshop Poster) | |
| Byzantine Fault-Tolerance of Local Gradient-Descent in Federated Model under 2f-Redundancy (Workshop Poster) | |