NeurIPS 2024 Past Large language modelsFederated learning
International Workshop on Federated Foundation Models in Conjunction with NeurIPS 2024
FL@FM-NeurIPS'24
- Submission deadline
- Oct 17, 2024, 12:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (25)
Fetched from OpenReview (v2) on 2026-06-10.
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$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning
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Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration
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Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning
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Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms
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DeComFL: Federated Learning with Dimension-Free Communication
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Defection-Free Collaboration between Competitors in a Learning System
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DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing
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Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
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EncCluster: Bringing Functional Encryption in Federated Foundational Models
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Enhancing Causal Discovery in Federated Settings with Limited Local Samples
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Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees
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Federated Learning with Generative Content
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FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator
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Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
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Hot Pluggable Federated Learning
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Improving Group Connectivity for Generalization of Federated Deep Learning
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Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models
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MAP: Model Merging with Amortized Pareto Front Using Limited Computation
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Momentum Approximation in Asynchronous Private Federated Learning
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On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
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OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning
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The Future of Large Language Model Pre-training is Federated
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The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain
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Worldwide Federated Training of Language Models
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ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning