federated-learning

The paper proposes OrchNAS, an energy-aware personalized federated edge intelligence framework that uses a Neural Architecture Search service to automatically design service-adaptive models for heterogeneous edge environments, addressing energy constraints and statistical heterogeneity.

QFedPolyp proposes a federated learning framework for polyp segmentation that uses quantization-aware training to reduce communication costs and achieve faster inference while preserving privacy.

This paper proposes Monotone FedNAM, a physically constrained federated additive model that ensures per-slice SLA risk predictions in O-RAN respect wireless physics (monotonicity) without pooling commercially sensitive KPI data, reducing uplink traffic by 65% while maintaining high shape consistency.

Forbes article explains split learning, a technique that enables AI models to train on sensitive data without exposing raw data, improving privacy compliance and reducing communication costs.

Proposes an analytic federated learning framework that requires only one or two communication rounds for multi-label medical image classification under task heterogeneity, outperforming existing methods on ChestXray14 by up to 18.44 BACC and 13.24 AUC points.

This paper introduces FLITE (Federated Low-rank Iterative Training Engine), a method for federated fine-tuning that reduces per-client communication to 1,280 floats per round (about 5KB) — an 8718× reduction over full-weight FedAvg — by using a frozen affine mapping network that generates weights from a small trainable latent and a low-rank seed-regenerable factorization, achieving accuracy within 0.5 percentage points of full-weight FedAvg on CIFAR-100 with ResNet-18.

FedCC proposes a federated learning framework combining a frozen DINOv2 backbone with a lightweight YOLO detection head and LoRA modules for robust corpus callosum localization in fetal ultrasound images, achieving strong performance with greatly reduced communication cost in a multi-center setting.

A federated learning research project reveals that global accuracy can mask catastrophic failure on minority attack classes in network intrusion detection, showing that per-client performance and aggregation method choice are critical for rare attack detection.

Proposes DiLoCo, a distributed optimization algorithm that enables training large language models across poorly connected devices with 500x less communication while matching fully synchronous performance.

HantaWatch is a federated learning framework for hantavirus genomic surveillance that enables collaborative training of sequence-based models without sharing raw data, integrating k-mer feature extraction and adaptive optimization to support risk screening and expert prioritization.

Proposes FedGAMMA, a federated multimodal graph foundation learning framework that aligns multimodal attributes and graph topology via two-stage pre-training and prompt-based fine-tuning, achieving significant gains on multiple datasets.

FLINT is a black-box framework that fingerprints FL model architectures (CNN, RNN, Transformer) using only 5G PHY-layer scheduling metadata, achieving 0.930 macro F1-score on an over-the-air testbed.

A systematic survey of Federated Explainable Artificial Intelligence (FedXAI), covering roles, architectures, evaluation practices, and open challenges. It presents a multi-axis taxonomy and discusses model-agnostic to interpretable-by-design approaches, highlighting gaps in standardization and privacy-aware evaluation.

Proposes FedCMM, a framework for federated continual learning of multimodal LLMs that uses modality-aware elastic weight consolidation, local generative replay, and task-similarity-aware gradient aggregation to mitigate catastrophic forgetting.

This paper introduces PFAdapter, a communication-efficient framework for personalized federated fine-tuning of Multimodal Large Language Models (MLLMs). It uses hierarchical LoRA decomposition to separate adapter parameters into global-shared and local-private components, achieving near 50% reduction in communication costs while improving personalization through orthogonality regularization.

FedCausal-Dyn is a novel federated learning framework that addresses dynamic feature drift by separating causal features from spurious variations, enabling robust prototype aggregation. It achieves state-of-the-art performance on federated domain generalization benchmarks.

Proposes FedKAD, a federated Koopman anomaly detection framework for multivariate time series in IoT systems, using lightweight sliding-window Koopman representations and a Stiefel-ADMM algorithm for efficient communication and inference.

Introduces HERO, a heterogeneity-aware benchmark library for federated continual learning that separates task splits, client data splits, and client task sequences to enable reproducible and setting-aware evaluation.

Introduces Collate, a training framework for collaborative neural network learning that handles heterogeneous edge devices with latency constraints, achieving accuracy improvements with minimal overhead.

PRoVeFL is a novel federated learning framework that achieves privacy-preserving, Byzantine-robust, and verifiable aggregation using multi-key fully homomorphic encryption, offering up to 100× runtime improvement over prior works.