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Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of FL research from 2023 to 2025, offering a unified taxonomy that categorizes works by data distribution (Horizontal FL, Vertical FL, Federated Transfer Learning, and Personalized FL), training synchronization (synchronous and asynchronous FL), optimization strategies, and threat models (data leakage and poisoning attacks). In particular, we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning, communication-efficient Horizontal FL, and domain-adaptive Federated Transfer Learning. Furthermore, we examine synchronization techniques addressing system heterogeneity, including straggler mitigation in synchronous FL and staleness management in asynchronous FL. The survey covers security threats in FL, such as gradient inversion, membership inference, and poisoning attacks, as well as their defense strategies that include privacy-preserving aggregation and anomaly detection. The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models, scalability, and real-world adoption.

Federated learning (FL) is a decentralized machine learning approach whereby each device is allowed to train local models, eliminating the requirement for centralized data collecting and ensuring data privacy. Unlike typical typical centralized machine learning, collaborative model training in FL involves aggregating updates from various devices without sending raw data. This ensures data privacy and security while collecting a collective learning from distributed data sources. These devices in FL models exhibit high efficacy in terms of privacy protection, scalability, and robustness, which is contingent upon the success of communication and collaboration. This paper explore the various topologies of both decentralized or centralized in the context of FL. In this respect, we investigated and explored in detail the evaluation of four widly used end-to-end FL frameworks: FedML, Flower, Flute, and PySyft. We specifically focused on vertical and horizontal FL systems using a logistic regression model that aggregated by the FedAvg algorithm. specifically, we conducted experiments on two images datasets, MNIST and Fashion-MNIST, to evaluate their efficiency and performance. Our paper provides initial findings on how to effectively combine horizontal and vertical solutions to address common difficulties, such as managing model synchronization and communication overhead. Our research indicates the trade-offs that exist in the performance of several simulation frameworks for federated learning.

Federated learning aims to collaboratively train a machine learning model with possibly geo-distributed workers, which is inherently communication constrained. To achieve communication efficiency, the conventional federated learning algorithms allow the worker to decrease the communication frequency by training the model locally for multiple times. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralised topologies and large nodes-to-server bandwidths, and convergence property relies on the stochastic gradient descent training in local, which usually causes the large end-to-end training latency in real-world federated learning scenarios. Thus, in this study, the authors propose the adaptive partial gradient aggregation method, a gradient partial level decentralised federated learning, to tackle this problem. In FedPGA, they propose a partial gradient exchange mechanism that makes full use of node-to-node bandwidth for speeding up the communication time. Besides, an adaptive model updating method further reduces the convergence rate by adaptive increasing the step size of the stable direction of gradient descent. The experimental results on various datasets demonstrate that the training time is reduced up to $14 \\times $14× compared to baselines without accuracy degrade.

Federated learning (FL) is a privacy-preserving distributed learning approach that allows multiple parties to jointly build machine learning models without disclosing sensitive data. Although FL has solved the problem of collaboration without compromising privacy, it has a significant communication overhead due to the repetitive updating of models during training. Several studies have proposed communication-efficient FL approaches to address this issue, but adequate solutions are still lacking in cases where parties must deal with different data features, also referred to as vertical federated learning (VFL). In this paper, we propose a communication-efficient approach for VFL that compresses the local data of clients, and then aggregates the compressed data from all clients to build an ML model. Since local data are shared in compressed form, the privacy of these data is preserved. Experiments on publicly available benchmark datasets using our proposed method show that the final model obtained by aggregation of compressed data from clients outperforms the performance of the local models of the clients.

Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client’s data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.

Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges such as data silos, streaming data, and data security. This survey explores OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning. Practical aspects of popular datasets and cutting-edge applications for online federated and transfer learning are also highlighted in this work. Furthermore, this survey provides insight into potential future research areas and aims to serve as a resource for professionals developing online federated and transfer learning frameworks.

Quantum federated learning (QFL) enables collaborative training of a quantum machine learning (QML) model among multiple clients possessing quantum computing capabilities, without the need to share their respective local data. However, the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing capabilities. This raises a natural question: Can quantum computing capabilities be deployed on the server instead? In this paper, we propose a QFL framework specifically designed for classical clients, referred to as CC-QFL, in response to this question. In each iteration, the collaborative training of the QML model is assisted by the shadow tomography technique, eliminating the need for quantum computing capabilities of clients. Specifically, the server constructs a classical representation of the QML model and transmits it to the clients. The clients encode their local data onto observables and use this classical representation to calculate local gradients. These local gradients are then utilized to update the parameters of the QML model. We evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST dataset. Our framework provides valuable insights into QFL, particularly in scenarios where quantum computing resources are scarce.

The rapid expansion of the Internet of Things (IoT) across critical sectors such as healthcare, energy, cybersecurity, smart cities, and finance has increased its exposure to cyberattacks. Conventional centralized machine learning-based Intrusion Detection Systems (IDS) face limitations, including data privacy risks, legal restrictions on cross-border data transfers, and high communication overhead. To overcome these challenges, we propose Privacy-Preserving Hierarchical Fog Federated Learning (PP-HFFL) for IoT intrusion detection, where fog nodes serve as intermediaries between IoT devices and the cloud, collecting and preprocessing local data, thus training models on behalf of IoT clusters. The framework incorporates a Personalized Federated Learning (PFL) to handle heterogeneous, non-independent, and identically distributed (non-IID) data and leverages differential privacy (DP) to protect sensitive information. Experiments on RT-IoT 2022 and CIC-IoT 2023 datasets demonstrate that PP-HFFL achieves detection accuracy comparable to centralized systems, reduces communication overhead, preserves privacy, and adapts effectively across non-IID data. This hierarchical approach provides a practical and secure solution for next-generation IoT intrusion detection.

The internet of things (IoT) represents a disruptive concept that has been changing society in several ways. There have been several successful applications of IoT in the industry. For example, in transportation systems, the novel internet of vehicles (IoV) concept has enabled new research directions and automation solutions. Moreover, reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated remarkable success in solving complex problems in different applications. In recent years, new solutions have been developed based on this combined framework (i.e., federated reinforcement learning). Conversely, there is a lack of analysis concerning IoT applications and a standard view of challenges and future directions of the current FRL landscape. Thereupon, the main goal of this research is to present a literature review of federated reinforcement learning (FRL) applications in IoT from multiple perspectives. We focus on analyzing applications in multiple areas (e.g., security, sustainability and efficiency, vehicular solutions, and industrial services) to highlight existing solutions, their characteristics, and research gaps. Additionally, we identify key short- and long-term challenges leading to new opportunities in the field. This research intends to picture the current FRL ecosystem in IoT to foster the development of new solutions based on existing challenges.