Federated Learning
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Benchmarks
Cityscapes heterogeneous
Landmarks-User-160k
Most implemented
Communication-Efficient Learning of Deep Networks from Decentralized Data
Federated Optimization in Heterogeneous Networks
Adaptive Personalized Federated Learning
Advances and Open Problems in Federated Learning
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
Adaptive Federated Optimization
Papers
vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning
Quantum federated learning (QFL) has emerged as a promising approach for collaboratively training compact quantum neural networks (QNNs) over distributed private data on resource-constrained devices. However, differences…
Neural Architecture SearchFederated LearningFedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices
Federated learning (FL) on heterogeneous edge devices must jointly accommodate unequal resource budgets and domain-shifted local data. Existing resource-adaptive methods decide how much of a model each client trains but …
Federated LearningFedLore: Communication and Memory Efficient Federated Learning via Shared Gradient Low-Rank Projection
Federated training of foundation models is constrained by client memory and communication costs. LoRA-based methods reduce these costs through low-rank adapters, but their fixed rank budget can limit adaptation. Gradient…
Federated LearningFedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization
Federated Learning (FL) enables privacy-preserving fine-tuning of Large Language Models (LLMs), yet the massive communication overhead remains a critical bottleneck. Furthermore, applying Low-Rank Adaptation (LoRA) in FL…
Federated LearningFedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains
Robust perception in intelligent vehicles demands 3D object detectors that remain dependable under domain shifts, such as changes in time of day, location, or weather. However, due to costly annotation and rare shifts, s…
Federated LearningStructural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation
Federated learning lets multiple participants train a shared model without pooling raw data, by exchanging locally trained model updates instead. Federated averaging assumes that averaging local models is a reasonable wa…
Federated Learning