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Federated Learning

Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
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Machine Learning Federated Learning 🏢 Nanjing University
Deep neural network training reveals asymmetric loss valleys, impacting model fusion and federated learning; sign consistency between noise and convergence is key.
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
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Machine Learning Federated Learning 🏢 Northeastern University
FedAWE, a novel federated learning algorithm, efficiently handles intermittent and unpredictable client availability, ensuring fast and unbiased model training.
Dual-Personalizing Adapter for Federated Foundation Models
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Natural Language Processing Federated Learning 🏢 Australian AI Institute
Federated Dual-Personalizing Adapter (FedDPA) tackles test-time distribution shifts and personalization in federated foundation models using a global and local adapter co-working mechanism, achieving …
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
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AI Generated Machine Learning Federated Learning 🏢 Beihang University
Dual Defense Federated Learning (DDFed) simultaneously boosts privacy and thwarts poisoning attacks in federated learning without altering the existing framework.
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation
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Machine Learning Federated Learning 🏢 Inria
DU-Shapley efficiently estimates the Shapley value for dataset valuation, enabling fair compensation in collaborative machine learning by leveraging the problem’s structure for faster computation.
Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences
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Machine Learning Federated Learning 🏢 King Abdullah University of Science and Technology
Boost federated learning efficiency! This paper introduces novel algorithms that cleverly combine gradient compression with random reshuffling, significantly reducing communication complexity and impr…
Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD
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AI Generated Machine Learning Federated Learning 🏢 North Carolina State University
A few high-performing agents using efficient sampling strategies can significantly boost the overall convergence speed of distributed machine learning algorithms, surpassing the performance of many mo…
Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors
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Machine Learning Federated Learning 🏢 Southern University of Science and Technology
Achieving egalitarian fairness in federated learning without sacrificing stability is possible; this paper derives optimal fairness bounds considering clients’ altruism and network topology.
DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans
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AI Generated Machine Learning Federated Learning 🏢 Zhejiang University
Attackers can steal massive private data from federated learning diffusion models using multiple Trojans and an advanced attack, AdaSCP, which circumvents existing defenses.
Data Acquisition via Experimental Design for Data Markets
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Machine Learning Federated Learning 🏢 MIT
Federated data acquisition via experimental design (DAVED) achieves lower prediction error without labeled validation data, optimizing cost-effectively for test-set predictions in decentralized market…
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
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Federated Learning 🏢 State Key Laboratory for Novel Software Technology
DapperFL enhances federated learning by introducing a model fusion pruning module and domain adaptive regularization to improve performance and reduce model size for heterogeneous edge devices.
Convergence Analysis of Split Federated Learning on Heterogeneous Data
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Machine Learning Federated Learning 🏢 Guangdong University of Technology
Split Federated Learning (SFL) convergence is analyzed for heterogeneous data, achieving O(1/T) and O(1/√T) rates for strongly convex and general convex objectives respectively. The study also extend…
Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data
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AI Generated Machine Learning Federated Learning 🏢 Central South University
CRFed, a novel federated learning framework, uses diffusion-based data harmonization and confusion-resistant strategies to significantly boost accuracy and robustness in non-IID data scenarios.
Communication-Efficient Federated Group Distributionally Robust Optimization
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Machine Learning Federated Learning 🏢 Texas A&M University
Communication-efficient algorithms for federated group distributionally robust optimization (FGDRO) are introduced, achieving lower communication complexity and superior performance on real-world task…
CoBo: Collaborative Learning via Bilevel Optimization
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Machine Learning Federated Learning 🏢 EPFL
CoBo: A novel bilevel optimization algorithm for collaborative learning surpasses existing methods by efficiently selecting helpful clients, resulting in superior performance and scalability.
Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences
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AI Generated Machine Learning Federated Learning 🏢 King Abdullah University of Science and Technology
Byzantine-tolerant Variance-Reduced MARINA with Partial Participation (Byz-VR-MARINA-PP) is the first distributed method to simultaneously achieve Byzantine robustness and partial client participation…
Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views
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AI Generated Machine Learning Federated Learning 🏢 School of Computer Science and Engineering, University of Electronic Science and Technology of China
FMCSC: A novel federated multi-view clustering framework bridging client and view gaps in heterogeneous hybrid views, achieving superior performance through local-synergistic contrastive learning and …
An Information Theoretic Perspective on Conformal Prediction
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AI Generated Machine Learning Federated Learning 🏢 Qualcomm AI Research
This paper uses information theory to improve conformal prediction, proving new ways to bound uncertainty and creating better training methods and side-information incorporation.
Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
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AI Generated Machine Learning Federated Learning 🏢 ETH Zurich
D-AdaST: A novel distributed adaptive minimax optimization method achieves near-optimal convergence by tracking stepsizes, solving the inconsistency problem hindering existing adaptive methods.
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
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Federated Learning 🏢 University of Sydney
A-FedPD tackles federated learning’s ‘dual drift’ problem by aligning global and local dual variables, resulting in faster convergence and enhanced stability for primal-dual methods.