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Federated Learning

551 directly classified papers

Papers per year

Papers

Scalable Federated Unlearning via Isolated and Coded Sharding IJCAI 2024
FedPFT: Federated Proxy Fine-Tuning of Foundation Models IJCAI 2024
Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains IJCAI 2024
Redefining Contributions: Shapley-Driven Federated Learning IJCAI 2024
Federated Graph Learning under Domain Shift with Generalizable Prototypes AAAI 2024
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching IJCAI 2024
Practical Hybrid Gradient Compression for Federated Learning Systems IJCAI 2024
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants AAAI 2024
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting NIPS 2024
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning NIPS 2024
Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets AAAI 2024
Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization AAAI 2024
A Kernel Perspective on Distillation-based Collaborative Learning NIPS 2024
Low Precision Local Training is Enough for Federated Learning NIPS 2024
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning NAACL 2024
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains NIPS 2024
DAGER: Exact Gradient Inversion for Large Language Models NIPS 2024
FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning IJCAI 2024
SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead NIPS 2024
DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting NIPS 2024
Thinking Forward: Memory-Efficient Federated Finetuning of Language Models NIPS 2024
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs NIPS 2024
On the Necessity of Collaboration for Online Model Selection with Decentralized Data NIPS 2024
On-Demand Federated Learning for Arbitrary Target Class Distributions AISTATS 2024
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations AAAI 2024