Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
Proposing Adaptive Coded Federated Learning (ACFL) to optimize privacy and learning performance in the presence of stragglers.
Decentralised Federated Learning: Initialisation and Topology Effects
Decentralised federated learning's effectiveness is influenced by network topology, requiring an improved initialisation strategy for artificial neural networks.
Effizientes Ressourcen-Effizientes Trainingsrahmenwerk für heterogene und ressourcenbeschränkte Umgebungen
Effizientes Ressourcenmanagement und Datenschutz in Federated Learning.
FedLoGe: Joint Local and Generic Federated Learning under Long-Tailed Data
Federated Local and Generic Model Training in Fed-LT (FedLoGe) enhances both local and generic model performance through representation learning and classifier alignment within a neural collapse framework.
FEDPIT: Privacy-preserving Federated Instruction Tuning for Few-shot Learning
FEDPIT proposes a novel federated algorithm that leverages large language models' in-context learning capability to generate task-specific synthetic data for training autonomously, improving federated few-shot performance while preserving privacy.
FEDPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning
FEDPIT nutzt LLMs zur Generierung synthetischer Daten für verbesserte Leistung und Datenschutz in der föderierten Anweisungsabstimmung.
PMFL: Partial Meta-Federated Learning for Heterogeneous Tasks and Real-World Medical Records
PMFL integrates federated learning and meta-learning to efficiently handle heterogeneous medical datasets, achieving superior performance.
One-Shot Clustering Using Data Similarity for Multi-Task Hierarchical Federated Learning
This research paper proposes a novel one-shot clustering algorithm for Multi-Task Hierarchical Federated Learning (MT-HFL) that leverages data similarity among users to improve accuracy and efficiency while preserving data privacy.
サブネットワークによるパーソナライズ化されたウォームアップによる、 향상된 이기종 연합 학습
データの異質性が極めて高い場合でも、パーソナライズされたウォームアップフェーズを導入することで、連合学習の収束速度と最終的な精度を向上させることができる。
Federated Learning via Hierarchical Clustered Sampling (HiCS-FL): Accelerating Convergence in Non-IID Settings by Targeting Client Data Heterogeneity
HiCS-FL, a novel client selection method for federated learning, accelerates model training convergence and reduces variance in non-IID settings by estimating and leveraging client data heterogeneity during the sampling process.
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