Workshop
Fri Jul 28 12:00 PM -- 08:00 PM (PDT) @ Meeting Room 311 None
Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities
Proposed around 2016 as privacy preserving techniques, federated learning and analytics (FL & FA) made remarkable progress in theory and practice in recent years. However, there is a growing disconnect between theoretical research and practical applications of federated learning. This workshop aims to bring academics and practitioners closer together to exchange ideas: discuss actual systems and practical applications to inspire researchers to work on theoretical and practical research questions that lead to real-world impact; understand the current development and highlight future directions. To achieve this goal, we aim to have a set of keynote talks and panelists by industry researchers focused on deploying federated learning and analytics in practice, and academic research leaders who are interested in bridging the gap between the theory and practice.
For more details, please visit the workshop webpage at https://fl-icml2023.github.io
| Introduction and Opening Remarks (opening) | |
| Vojta Jina: Lessons from Applying Private Federated Learning (Invited Talk) | |
| Two Spotlight Talks (Spotlight Talks) | |
| Break | |
| Li Xiong: Federated Learning with Personalized and User-level Differential Privacy (Invited Talk) | |
| Brendan McMahan: Advances in Privacy and Federated Learning, with Applications to GBoard (Invited Talk) | |
| Poster and Lunch (Poster) | |
| Panel Discussion (Panel) | |
| Ce Zhang: Optimizing Communications and Data for Distributed and Decentralized Learning (Invited Talk) | |
| Break | |
| Giulia Fanti: New Variants of Old Challenges in Data Valuation and Privacy (Invited Talk) | |
| Three Spotlight Talks (Spotlight Talks) | |
| Chuan Guo: Towards (Truly) Private and Communication-efficient Federated Learning (Invited Talk) | |
| Concluding Remarks (Concluding) | |
| Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data (Poster) | |
| Privacy Auditing with One (1) Training Run (Poster) | |
| Sketch-and-Project Meets Newton Method: \\ Global $\mathcal O \left( k^{-2} \right)$ Convergence with Low-Rank Updates (Poster) | |
| A Joint Training-Calibration Framework for Test-Time Personalization with Label Distribution Shift in Federated Learning (Poster) | |
| Adaptive Federated Learning with Auto-Tuned Clients (Poster) | |
| Learning-augmented private algorithms for multiple quantile release (Poster) | |
| Unleashing the Power of Randomization in Auditing Differentially Private ML (Poster) | |
| Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning (Poster) | |
| Don’t Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory (Poster) | |
| Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning (Poster) | |
| Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning (Poster) | |
| FedFwd: Federated Learning without Backpropagation (Poster) | |
| Randomized Quantization is All You Need for Differential Privacy in Federated Learning (Poster) | |
| Federated Experiment Design under Distributed Differential Privacy (Poster) | |
| Exact Optimality in Communication-Privacy-Utility Tradeoffs (Poster) | |
| Federated, Fast, and Private Visualization of Decentralized Data (Poster) | |
| Fast and Communication Efficient Decentralized Learning with Local Updates (Poster) | |
| ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression (Poster) | |
| A New Theoretical Perspective on Data Heterogeneity in Federated Optimization (Poster) | |
| Towards a Theoretical and Practical Understanding of One-Shot Federated Learning with Fisher Information (Poster) | |
| Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning (Poster) | |
| Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration (Poster) | |
| Guiding The Last Layer in Federated Learning with Pre-Trained Models (Poster) | |
| Green Federated Learning (Poster) | |
| A Convergent Federated Clustering Algorithm without Initial Condition (Poster) | |
| $\texttt{FED-CURE}$: A Robust Federated Learning Algorithm with Cubic Regularized Newton (Poster) | |
| Resource-Efficient Federated Learning (Poster) | |
| Concept-aware clustering for decentralized deep learning under temporal shift (Poster) | |
| Population Expansion for Training Language Models with Private Federated Learning (Poster) | |
| Neighborhood Gradient Clustering: An Efficient Decentralized Learning Method for Non-IID Data (Poster) | |
| Strategic Data Sharing between Competitors (Poster) | |
| Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation (Poster) | |
| FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning (Poster) | |
| Leveraging Side Information for Communication-Efficient Federated Learning (Poster) | |
| Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes (Poster) | |
| Improving Accelerated Federated Learning with Compression and Importance Sampling (Poster) | |
| Momentum Provably Improves Error Feedback! (Poster) | |
| Federated Learning with Regularized Client Participation (Poster) | |
| Federated Optimization Algorithms with Random Reshuffling and Gradient Compression (Poster) | |
| Clustering-Guided Federated Learning of Representations (Poster) | |
| Distributed Mean Estimation for Multi-Message Shuffled Privacy (Poster) | |
| Federated Conformal Predictors for Distributed Uncertainty Quantification (Poster) | |
| Distributed Architecture Search over Heterogeneous Distributions (Poster) | |
| On the Performance of Gradient Tracking with Local Updates (Poster) | |
| Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation (Poster) | |
| On the Still Unreasonable Effectiveness of Federated Averaging for Heterogeneous Distributed Learning (Poster) | |
| Differentially Private Heavy Hitters using Federated Analytics (Poster) | |
| Beyond Secure Aggregation: Scalable Multi-Round Secure Collaborative Learning (Poster) | |
| On Differentially Private Federated Linear Contextual Bandits (Poster) | |
| Towards a Better Theoretical Understanding of Independent Subnetwork Training (Poster) | |
| Federated Heavy Hitter Recovery under Linear Sketching (Poster) | |
| Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation (Poster) | |
| Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Information Theory (Poster) | |
| Federated Ensemble-Directed Offline Reinforcement Learning (Poster) | |
| Machine Learning with Feature Differential Privacy (Poster) | |
| SCAFF-PD: Communication Efficient Fair and Robust Federated Learning (Poster) | |