A Hybrid Federated Learning Framework with Dynamic Task ...

A Hybrid Federated Learning Framework With Dynamic Task ...

We propose a hybrid federated learning framework for multi-party distributed load prediction. We seamlessly integrate horizontal and vertical federated ...

Index Terms—Distributed load prediction, dynamic task allo- cation, federated learning, XGBoost. I. INTRODUCTION. LOAD prediction has become an increasingly ...

(PDF) A Hybrid Federated Learning Framework With Dynamic Task ...

Abstract · 1) We propose a hybrid federated learning framework based. on XGBoost. The hybrid nature of the framework can · 2) We propose a ...

A Hybrid Federated Learning Framework with Dynamic Task ...

In distributed load prediction problems, training datasets on load consumption and load-related features are scattered within various districts and parties.

A Fair and Efficient Hybrid Federated Learning Framework based on ...

Moreover, we design a dynamic task allocation scheme such that each party gets a fair share of information, and the computing power of each ...

FedCD: A Hybrid Federated Learning Framework for Efficient ...

Request PDF | FedCD: A Hybrid Federated Learning Framework for Efficient Training With IoT Devices | With billions of IoT devices producing ...

Efficient Federated Learning Using Dynamic Update and Adaptive ...

Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of ...

Federated learning and its application in smart cities

This research was published in two papers entitled “A Hybrid Federated Learning Framework with Dynamic Task Allocation for Multi-Party ...

Multi-task federated learning-based system anomaly detection and ...

Propose a multi-task federated learning model to identify anomalies in microservices. •. Proficiently capture intricate abnormal patterns and features ...

SMILELab-FL/FedLab: A flexible Federated Learning Framework ...

A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research. - SMILELab-FL/FedLab.

FedHD: Communication-efficient federated learning from hybrid data

In this paper, we propose a new FL algorithm, termed as FedHD, that can solve the challenging learning task under the HBFL setting.

A Federated Learning Framework for Smart Grids: Securing Power ...

A hybrid federated learning framework based on XGBoost is ... dynamic task allocation scheme such that each party gets a fair share of information.

A Blockchain-based federated learning framework for secure ...

Malicious nodes engage in the training task by altering local data or local model parameters, which reduces the efficacy of federated learning. Alternatively, ...

Federated learning - Wikipedia

Federated learning is a machine learning technique focusing on settings in which multiple entities collaboratively train a model while ensuring that their ...

RaftFed: A Lightweight Federated Learning Framework for Vehicular ...

dynamic clustering and hybrid federated learning mechanism to improve Non-IID performance degradation and meanwhile accelerate model convergence ...

FedHiSyn: A Hierarchical Synchronous Federated Learning ...

In addition, the data heterogeneity incurs severe accuracy degradation of the global model in the FL training process. To address aforementioned issues, we ...

IEEE Transactions on Big Data - Table of Contents

A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning pp. ... DCLCSE: Dynamic Curriculum Learning Based Contrastive Learning of ...

A Federated Learning Multi-Task Scheduling Mechanism Based on ...

The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the ...

[PDF] Semi-Federated Learning | Semantic Scholar

FedSeq: A Hybrid Federated Learning Framework Based on Sequential In-Cluster Training ... dynamic cluster assignment with improved inner-cluster vehicle ...

FedMDP: A Federated Learning Framework to Handle System and ...

Finally, on the third phase, we integrate the distillation technique and dynamic task allocation tech- nique to handle model and system heterogeneity of the net ...