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Federated Learning

1,360 papers

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Papers

CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning WACV 2026

SD-CSFL: A Synthetic Data-Driven Conformity Scoring Framework for Robust Federated Learning WACV 2026

Federated Model Synchronization for Diagnostic Redefinition through a Novel Selective Parameter Unlearning WACV 2026

Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains WACV 2026

AFL-PRF: Adaptive Federated Learning for Low-Quality Data: Enhancing Performance, Robustness, and Fairness WACV 2026

FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels WACV 2026

Personalized Image Privacy Advisors via Federated Daisy-Chaining WACV 2026

HEART-PFL: Stable Personalized Federated Learning under Heterogeneity with Hierarchical Directional Alignment and Adversarial Knowledge Transfer WACV 2026

Marshaled Learning: Bridging Large Neural Networks with Memory-Constrained Trusted Execution Environments in Federated Learning WACV 2026

FedSCAl: Leveraging Server and Client Alignment for Unsupervised Federated Source-Free Domain Adaptation WACV 2026

Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Models WACV 2026

RB-LoRA: Rank-Balanced Aggregation for Low-Rank Adaptation with Federated Fine-Tuning EACL 2026

FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation EACL 2026

Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-temporal Graph Learning Method for Traffic Flow Forecasting AAAI 2026

Good Gradients Poison Your Model: Evading Defenses in Federated Learning via Boundary-adaptive Perturbation AAAI 2026

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach AAAI 2026

Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendation AAAI 2026

FedGRPO: Privately Optimizing Foundation Models with Group-Relative Rewards from Domain Clients AAAI 2026

PAGE: A Unified Approach for Federated Graph Unlearning AAAI 2026

FedALT: Federated Fine-Tuning Through Adaptive Local Training with Rest-of-World LoRA AAAI 2026

PPFL: A Parameter Behavior-Driven Plug-in Personalization Engine for Federated Learning AAAI 2026

FedMerge: Federated Model Merging for Personalization AAAI 2026

Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants AAAI 2026

Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size? AAAI 2026

LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping AAAI 2026