federated-learning

The paper argues that federated learning is not a complete remedy for extractive AI, focusing on governance layers for creative communities and proposing design principles for a data commons that governs models and federation.

This paper proposes SAPE-FL, a similarity-aware personalized federated learning framework that adapts to heterogeneous environments by anchoring models to global and peer-averaged models, improving robustness and performance.

This paper benchmarks federated LoRA adaptation of BiomedCLIP for chest X-ray classification across four international cohorts, demonstrating improved performance over unadapted models and approaching centralized training results.

FedRoRA is a novel framework for personalized federated LoRA fine-tuning that addresses rank heterogeneity and data heterogeneity in federated learning by decoupling adaptation into shared global directions and personalized magnitudes.

The paper proposes CRAD, a class-wise reliability-aware distillation method for decentralized federated learning to handle heterogeneous architectures and non-IID data, achieving improved accuracy on image classification benchmarks.

This paper introduces a novel self-consistent midpoint aggregation method for stable quantum federated learning, addressing challenges like data heterogeneity and quantum noise with validation on real quantum machines.

The article discusses using NVIDIA FLARE to build federated multimodal AI workflows, addressing challenges with distributed data through techniques like externalization and tensor streaming to optimize model distribution and reduce memory requirements.

The paper proves that in stochastic SCAFFOLD for federated learning, client averaging suppresses the leading stationary bias but leaves a client-independent second-order bias component due to control fluctuations and nonquadratic curvature.

This paper proposes a unified framework for decentralized learning under communication constraints, introducing the DMFL-SQ algorithm that combines graph-based personalization, agnostic fairness, and compressed communication to achieve reduced communication while maintaining predictive performance and improving fairness across clients.

FedCMAPSS introduces a benchmark for federated learning in remaining useful life estimation based on the NASA C-MAPSS dataset, with standardized tasks to evaluate federated optimization algorithms across neural architectures.

This paper analyzes the transferability of adversarial attacks in federated learning systems and proposes a defense mechanism based on adversarial training to enhance model robustness.

Flower Hub is a reproducible benchmarking platform for federated learning that enables execution and evaluation across both simulation and deployment runtimes.

Thermo-FL is a federated LoRA fine-tuning framework for large language models on edge devices that uses device temperature to regulate training and transmission, paired with a robust aggregation method to defend against adversarial attacks, enhancing stability and performance.

The paper proposes FlatLand, a personalized federated learning method that uses tailored Lorentz space in hyperbolic geometry to handle heterogeneous graph structures among clients, improving performance in privacy-preserving collaborative training.

FL-MAESTRO introduces a multi-agent LLM orchestrator to jointly optimize communication topology, resource allocation, and aggregation rules in federated learning, significantly reducing wasted energy and improving efficiency on resource-constrained edge devices.

This paper proposes FedCurv-DR, a lightweight federated continual learning method that reduces forgetting and balances performance, fairness, and energy efficiency for sustainable AI in cultural heritage applications.

This tweet describes federated learning as a privacy-preserving method to train shared AI models across data silos without moving raw data, emphasizing architectural decisions for compliance, and mentions using Hoppscotch for API testing.

FedLNS is a server-side framework that uses LayerNorm signatures to screen malicious updates in federated learning for language models, enhancing robustness against adversarial manipulation.

This paper studies federated active learning in low-budget regimes, revealing that homogeneous data requires stronger coordination due to heterogeneity reversal. It proposes a framework using federated representation learning to enable globally coordinated active selection, outperforming existing methods.

FedPref introduces a federated preference learning method for structured radiology report extraction, allowing institutions to collaborate without sharing data while improving extraction accuracy with gains in F1 scores on simulated hospitals.