Top Research Papers on Federated Learning

Federated learning is a set-up in which multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggregator. This setting also allows the training data decentralized to ensure the data privacy of each device. Federated learning adheres to two major ideas: local computing and model transmission, which reduces some systematic privacy risks and costs brought by traditional centralized machine learning methods. The original data of the client is stored locally and cannot be exchanged or migrated. With the application of federated learning, each ...

A comprehensive survey of the unique security vulnerabilities exposed by the FL ecosystem is provided, highlighting the vulnerabilities sources, key attacks on FL, defenses, as well as their unique challenges, and discussing promising future research directions towards more robust FL.

A background and comparison of centralized learning, distributed on-site learning, and FL, which is then followed by a survey of the application of FL to cybersecurity for IoT, so readers can have a more thorough understanding of FL for cybersecurity as well as cybersecurity for FL, different security attacks, and countermeasures.

A horizontal-based federated learning architecture, empowered by fog federations, devised for the mobile environment is proposed and results show that the proposed model can achieve better accuracy and quality of service than other models presented in the literature.

A novel Global-Local Forgetting Compensation (GLFC) model is developed, to learn a global class-incremental model for alleviating the catastrophic forgetting from both local and global perspectives, and a prototype gradient-based communication mechanism is developed to protect the privacy.

A personalized variant of the well-known Federated Averaging algorithm is studied and its performance is characterized by the closeness of underlying distributions of user data, measured in terms of distribution distances such as Total Variation and 1-Wasserstein metric.

The incentive mechanism for federated learning to motivate edge nodes to contribute model training is studied and a deep reinforcement learning-based (DRL) incentive mechanism has been designed to determine the optimal pricing strategy for the parameter server and the optimal training strategies for edge nodes.

While FL appears to be a promising Machine Learning technique to keep the local data private, it is also vulnerable to attacks like other ML models.

Experimental results show that the federated drone authentication model gains a high true positive rate (TPR) during drone authentication and better performance compared to other ML-based models.

This work proposes the federated fair averaging (FedFV) algorithm to mitigate potential conflicts among clients before averaging their gradients, and shows the theoretical foundation of FedFV to mitigate the issue conflicting gradients and converge to Pareto stationary solutions.

This study reviews FL and explores the main evolution path for issues exist in FL development process to advance the understanding of FL, and identifies six research fronts to address FL literature and help advance theUnderstanding of FL for future optimization.

This work identifies the unique characteristics associated with conveying trained models over rate-constrained channels, and characterize a suitable quantization scheme for such setups, and shows that combining universal vector quantization methods with FL yields a decentralized training system, which is both efficient and feasible.

The proposed FedDetect learning framework improves the performance by utilizing a local adaptive optimizer and a cross-round learning rate scheduler, and the system efficiency analysis indicates that both end-to-end training time and memory cost are affordable and promising for resource-constrained IoT devices.

This survey explores the domain of personalized FL (PFL) to address the fundamental challenges of FL on heterogeneous data, a universal characteristic inherent in all real-world datasets.

A taxonomy of recent attacks on federated learning systems is provided and the need for more robust threat modeling in Federated learning environments is detailed.

Information theoretically, it is proved that the mixture of local and global models can reduce the generalization error and a communication-reduced bilevel optimization method is proposed, which reduces the communication rounds to $O(\sqrt{T})$ and can achieve a convergence rate of $O(1/T)$ with some residual error.

The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space, and summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federation, and point out the implications and potentials in healthcare.

This article provides a comprehensive overview of the relationship between Federated learning and wireless communications, including basic principles of federated learning, efficient communications for training a federatedLearning model, and federatedlearning for intelligent wireless applications.

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federa...

A communication-efficient FL framework is proposed to jointly improve the FL convergence time and the training loss, and a probabilistic device selection scheme is designed such that the devices that can significantly improve the convergence speed and training loss have higher probabilities of being selected for ML model transmission.

This paper provides a concise introduction to the concept of FL, and a unique taxonomy covering threat models and two major attacks on FL: 1) poisoning attacks and 2) inference attacks, and provides an accessible review of this important topic.

This work proposes Federated matched averaging (FedMA) algorithm designed for federated learning of modern neural network architectures e.g. convolutional neural networks (CNNs) and LSTMs and indicates that FedMA outperforms popular state-of-the-art federatedLearning algorithms on deep CNN and L STM architectures trained on real world datasets, while improving the communication efficiency.

MOON is a simple and effective federated learning framework that utilizes the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level.

A description of federated neural architecture search that has recently been proposed is proposed, which is categorized into online and offline implementations, and single- and multi-objective search approaches.

A novel approach is presented, named splitfed learning (SFL), that amalgamates the two approaches eliminating their inherent drawbacks, along with a refined architectural configuration incorporating differential privacy and PixelDP to enhance data privacy and model robustness.

DISTPAB alleviates computational bottlenecks by distributing the task of privacy preservation utilizing the asymmetry of resources of a distributed environment, which can have resource-constrained devices as well as high-performance computers.

This work proposes a new framework for robust federated learning where the central server learns to detect and remove the malicious model updates using a powerful detection model, leading to targeted defense.

This work proposes FCCL (Federated CrossCorrelation and Continual Learning), which leverages unlabeled public data for communication and construct cross-correlation matrix to learn a generalizable representation under domain shift for heterogeneity problem and catastrophic forgetting.

Surprisingly, the best gossip variants perform comparably to the best federated learning variants overall, thus providing a fully decentralized alternative to federatedLearning.

The SecureBoost framework is shown to be as accurate as other nonfederated gradient tree-boosting algorithms that require centralized data, and thus, it is highly scalable and practical for industrial applications such as credit risk analysis.

The FL incentivizer (FLI) dynamically divides a given budget in a context-aware manner among data owners in a federation by jointly maximizing the collective utility while minimizing the inequality among the data owners, in terms of the payoff received and the waiting time for receiving payoffs.

This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible.

This paper proposes a robust design for federated learning to decline the effect of noise and utilizes the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function.

Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive local datasets with others. Despite its benefits, FL is vulnerable to so-called backdoor attacks, in which an adversary injects manipulated model updates into the federated model aggregation process so that the resulting model will provide targeted false predictions for specific adversary-chosen inputs. Proposed defenses against backdoor attacks based on detecting and filtering out malicious model updates consider only ...

This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.

This manuscript presents OpenFL and summarizes its motivation and development characteristics, with the intention of facilitating its application to existing ML/DL model training in a production environment, and describes the first real-world healthcare federations that use the OpenFL library.

Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair (i.e., more uniform) accuracy distribution across devices in federated networks. To solve q-FFL, we devise a communication-efficient method, q-FedAvg, that is suited to federated networks. We valid...

A UAV-enabled covert federated learning architecture, where the UAV is not only responsible for orchestrating the operation of FL but also for emitting artificial noise to interfere with the eavesdropping of unintended users is conceived.

This research presents an engineering and algorithmic framework to ensure data privacy and user confidentiality in the rapidly changing environment.

A peer-to-peer Deep Federated Learning (DFL) approach to train deep architectures in a fully decentralized manner and remove the need for central orchestration is proposed and achieves superior accuracy compared with other recent methods.