Generative Models for Personalized Federated Learning via Class-Separable Latent Spaces and Meta-Adaptation

In the era of computational social systems (CSS), where AI is increasingly embedded in critical domains such as public services, healthcare, and social governance, concerns about system vulnerabilities have become paramount. Federated learning (FL) offers a promising solution by enabling privacy-preserving and secure AI model training in real-world applications. However, traditional FL approaches often assume that client data is either imbalanced or non-independent and identically distributed (non-IID), whereas in practice, these issues frequently coexist. In such complex scenarios, minority classes may appear only on a small subset of clients, significantly degrading global model performance and convergence. In this article, we propose a novel FL framework designed to tackle the intertwined challenges of data imbalance and non-IID data by a principled, decoupled approach. Unlike existing methods that address these issues in isolation, our approach introduces three synergistic innovations, each operating at a different level of the FL system. At the data level, a global rebalancing mechanism employs a privacy-preserving modified Z-score to rectify class imbalance. At the client level, a distribution-aware clustering strategy groups participants based on data subspace similarity. At the feature level, a novel contrastive loss function, leveraging the effective number of samples, simultaneously mitigates local imbalance and model drift. This cohesive framework ensures that interventions at each level reinforce each other, leading to robust performance. Extensive experiments show that FedScFc outperforms state-of-the-art baselines.

This article provides a comprehensive survey of aggregation strategies in federated learning (FL). This decentralized machine learning (ML) paradigm enables multiple clients to collaboratively train models without sharing their local datasets. Aggregation is a pivotal aspect of FL, as it integrates model updates from diverse clients into a unified global model while addressing critical challenges such as data heterogeneity, scalability, and privacy preservation. The study categorizes aggregation strategies into three primary approaches: data-centric, model-centric, and secure aggregation, each tailored to address distinct problems in FL systems. Data-centric strategies focus on addressing non-independent and identically distributed (non-IID) data across clients, ensuring that model updates account for imbalances in data distributions. Model-centric strategies optimize the aggregation of model parameters, emphasizing techniques such as weighted averaging and model distillation to improve model performance across clients with diverse data characteristics. Secure aggregation techniques aim to enhance privacy and robustness, protecting client data from potential adversarial threats through encryption-based methods like secure multi-party computation (SMPC) and Byzantine-resilient techniques. The analysis delves into the advantages and limitations of these aggregation strategies, particularly their role in tackling challenges like non-IID data, communication efficiency, and resistance to adversarial attacks. Furthermore, the article identifies existing research gaps in FL, including the need for more scalable and robust aggregation methods capable of reducing communication costs, enhancing privacy guarantees, and improving performance in highly heterogeneous environments. These insights provide a roadmap for future research aimed at advancing aggregation strategies in FL to improve model accuracy, security, and efficiency in real-world applications.

This study examines the benefits of applying federated learning (FL) technology to OPC (Operational Performance Control) systems within industrial automation and data analysis processes. FL enables each production facility to process its data locally while only transmitting model parameters to a central server, thereby preserving data privacy. This approach provides significant advantages in industrial environments, particularly concerning data privacy and communication costs. The study evaluates FL's potential to ensure data privacy, reduce communication costs, improve efficiency in training time, and deliver high performance in predictive maintenance and quality estimation. Model performance was analyzed using accuracy, F1 score, precision, and loss metrics; the results demonstrated that FL achieved a 90% accuracy rate, offering competitive performance compared to centralized modeling. In predictive maintenance and quality analysis specifically, FL achieved 85-88% accuracy while reducing network data load by 65%. These findings validate that FL provides a secure, cost-effective, and efficient solution for industrial data analysis processes by eliminating the need for centralized data collection. In conclusion, FL and OPC integration supports data privacy, cost savings, and communication efficiency in industrial processes. The study highlights that FL could become a prevalent technology in industrial data analysis, establishing a new standard particularly in digital manufacturing processes.

Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing

Mobile edge computing aims to deploy mobile applications at the edge of wireless networks. Federated learning in mobile edge computing is a forward-looking distributed framework for deploying deep learning algorithms in many application scenarios. One challenge of federated learning in mobile edge computing is data heterogeneity since the unified model of federated learning performs poorly when client data are non-independent and identically distributed. Personalized federated learning can obtain amazing models in scenarios where client data are non-independent and identically distributed. This is because the personalized model captures the features of users’ local data more accurately than the unified model. The personalized federated learning problem under two-tier (server-client) federated learning structures has been widely studied and applied. However, a lot of research results exhibit three distinct limitations: 1) suboptimal communication efficiency, 2) slow model convergence, and 3) underutilization of the relationships within user data, resulting in lower accuracy of personalized models. In this paper, we present the first personalized federated learning algorithm based on the client-edge-cloud structure. The edge server is responsible for model personalization and employs a learnable mixing parameter to mix the local model and the global model. We also utilize two learnable normalization parameters trained by clients to improve the performance of personalized models. Furthermore, in order to facilitate the collaboration among edge servers, we propose a similarity aggregation method to assign aggregation weights based on the Tanimoto coefficients between models. The experimental results show that the proposed algorithm not only increases the convergence speed of personalized models but also improves their testing accuracy.

Rice (Oryza sativa L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features.

Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. In real-world FL implementations, client data could have label noise, and different clients could have vastly different label noise levels. Although there exist methods in centralized learning for tackling label noise, such methods do not perform well on heterogeneous label noise in FL settings, due to the typically smaller sizes of client datasets and data privacy requirements in FL. In this paper, we propose FedCorr, a general multi-stage framework to tackle heterogeneous label noise in FL, without making any assumptions on the noise models of local clients, while still maintaining client data privacy. In particular, (1) FedCorr dynamically identifies noisy clients by exploiting the dimensionalities of the model prediction subspaces independently measured on all clients, and then identifies incorrect labels on noisy clients based on persample losses. To deal with data heterogeneity and to increase training stability, we propose an adaptive local proximal regularization term that is based on estimated local noise levels. (2) We further finetune the global model on identified clean clients and correct the noisy labels for the remaining noisy clients after finetuning. (3) Finally, we apply the usual training on all clients to make full use of all local data. Experiments conducted on CIFAR-10/100 with federated synthetic label noise, and on a real-world noisy dataset, Clothing1M, demonstrate that FedCorr is robust to label noise and substantially outperforms the state-of-the-art methods at multiple noise levels.

Federated learning (FL) is a promising collaborative learning approach in edge computing, reducing communication costs and addressing the data privacy concerns of traditional cloud-based training. Owing to this, diverse studies have been conducted to distribute FL into industry. However, there still remain the practical issues of FL to be solved (e.g., handling non-IID data and stragglers) for an actual implementation of FL. To address these issues, in this paper, we propose a cluster-driven adaptive training approach (CATA-Fed) to enhance the performance of FL training in a practical environment. CATA-Fed employs adaptive training during the local model updates to enhance the efficiency of training, reducing the waste of time and resources due to the presence of the stragglers and also provides a straggler mitigating scheme, which can reduce the workload of straggling clients. In addition to this, CATA-Fed clusters the clients considering the data size and selects the training participants within a cluster to reduce the magnitude differences of local gradients collected in the global model update under a statistical heterogeneous condition (e.g., non-IID data). During this client selection process, a proportional fair scheduling is employed for securing the data diversity as well as balancing the load of clients. We conduct extensive experiments using three benchmark datasets (MNIST, Fashion-MNIST, and CIFAR-10), and the results show that CATA-Fed outperforms the previous FL schemes (FedAVG, FedProx, and TiFL) with regard to the training speed and test accuracy under the diverse FL conditions.

Privacy and data security have become the new hot topic for regulators in recent years. As a result, Federated Learning (FL) (also called collaborative learning) has emerged as a new training paradigm that allows multiple, geographically distributed nodes to learn a Deep Learning (DL) model together without sharing their data. Blockchain is becoming a new trend as data protection and privacy are concerns in many sectors. Technology is leading the world and transforming into a global village where everything is accessible and transparent. We have presented a blockchain enabled security model using FL that can generate an enhanced DL model without sharing data and improve privacy through higher security and access rights to data. However, existing FL approaches also have unique security vulnerabilities that malicious actors can exploit and compromise the trained model. The FL method is compared to the other known approaches. Users are more likely to choose the latter option, i.e., providing local but private data to the server and using ML apps, performing ML operations on the devices without benefiting from other users’ data, and preventing direct access to raw data and local training of ML models. FL protects data privacy and reduces data transfer overhead by storing raw data on devices and combining locally computed model updates. We have investigated the feasibility of data and model poisoning attacks under a blockchain-enabled FL system built alongside the Ethereum network and the traditional FL system (without blockchain). This work fills a knowledge gap by proposing a transparent incentive mechanism that can encourage good behavior among participating decentralized nodes and avoid common problems and provides knowledge for the FL security literature by investigating current FL systems.

Federated learning has been popular for its ability to train centralized models while protecting clients' data privacy. However, federated learning is highly susceptible to poisoning attacks, which can result in a decrease in model performance or even make it unusable. Most existing defense methods against poisoning attacks cannot achieve a good trade-off between robustness and training efficiency, especially on non-IID data. Therefore, this paper proposes an adaptive model filtering algorithm based on the Grubbs test in federated learning (FedGaf), which can achieve great trade-offs between robustness and efficiency against poisoning attacks. To achieve a trade-off between system robustness and efficiency, multiple child adaptive model filtering algorithms have been designed. Meanwhile, a dynamic decision mechanism based on global model accuracy is proposed to reduce additional computational costs. Finally, a global model weighted aggregation method is incorporated, which improves the convergence speed of the model. Experimental results on both IID and non-IID data show that FedGaf outperforms other Byzantine-robust aggregation rules in defending against various attack methods.

Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity among their local data distributions, which would cause inconsistent optimized local models on the clientside. To address this fundamental dilemma, we propose a novel federated learning algorithm with local drift decoupling and correction (FedDC). Our FedDC only introduces lightweight modifications in the local training phase, in which each client utilizes an auxiliary local drift variable to track the gap between the local model parameter and the global model parameters. The key idea of FedDC is to utilize this learned local drift variable to bridge the gap, i.e., conducting consistency in parameter-level. The experiment results and analysis demonstrate that FedDC yields expediting convergence and better performance on various image classification tasks, robust in partial participation settings, non-iid data, and heterogeneous clients.

Federated learning (FL) protects data privacy by enabling distributed model training without direct access to client data. However, its distributed nature makes it vulnerable to model and data poisoning attacks. While numerous defenses filter malicious clients using statistical metrics, they overlook the role of model redundancy, where not all parameters contribute equally to the model and attack performance. Current attacks manipulate all model parameters uniformly, making them more detectable, while defenses focus on the overall statistics of client updates, leaving gaps for more sophisticated attacks. We propose an attack-agnostic augmentation method to enhance the stealthiness and effectiveness of existing poisoning attacks in FL, exposing flaws in current defenses and highlighting the need for fine-grained FL security. Our three-stage methodology, including pill construction, pill poisoning, and pill injection, injects poison into a compact subnet (i.e., pill) of the global model during the iterative FL training. Experimental results show that FL poisoning attacks enhanced by our method can bypass 8 state-of-the-art (SOTA) defenses, gaining an up to 7x error rate increase, as well as on average a more than 2x error rate increase on both IID and non-IID data, in both cross-silo and cross-device FL systems.

Federated Learning (FL) has emerged as a pivotal framework for privacy-preserving collaborative model training, especially in domains such as healthcare, telecommunication, finance where data sensitivity is paramount. While traditional FL techniques like Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD) offer simplicity and efficiency, they often struggle in heterogeneous data environments due to equal treatment of all client contributions, regardless of quality. This leads to degraded global model performance, high communication overhead, and slow convergence, particularly under non-IID data and client variability. To address these limitations, we propose Performance-Based Federated Partial Averaging (FedPA), a novel aggregation method that selectively incorporates updates from high-performing clients based on local accuracy and F1-score thresholds. Unlike FedAvg, FedPA excludes low-performing updates from global aggregation, while still allowing underperforming clients to receive global model updates for progressive learning. This approach mitigates the adverse effects of noisy contributions, accelerates convergence, and reduces communication overhead. We validate FedPA using the MNIST dataset under a simulated non-IID federated setting. Experimental results show that FedPA achieves competitive accuracy and F1 scores with significantly fewer communication rounds compared to FedAvg. Additionally, FedPA demonstrates robustness to client-side class imbalance and data heterogeneity, outperforming baselines in fairness, convergence stability, and communication efficiency. Our findings suggest that performance-aware aggregation strategies like FedPA hold strong potential for scalable and equitable deployment of FL systems in real-world, data-sensitive applications. Especially in 5G wireless networks, where optimized resource allocation and communication efficiency are essential, FedPA effectively aligns performance-driven aggregation with resource constraints, paving the way for intelligent and adaptive FL deployment across edge-cloud infrastructures.

A large number of mobile devices, smart wearable devices, and medical and health sensors continue to generate massive amounts of data, making edge devices’ data explode and making it possible to implement data-driven artificial intelligence. However, the “data silos” and other issues still exist and need to be solved. Fortunately, federated learning (FL) can deal with “data silos” in the medical field, facilitating collaborative learning across multiple institutions without sharing local data and avoiding user concerns about data privacy. However, it encounters two main challenges in the medical field. One is statistical heterogeneity, also known as non-IID (non-independent and identically distributed) data, i.e., data being non-IID between clients, which leads to model drift. The second is limited labeling because labels are hard to obtain due to the high cost and expertise requirement. Most existing federated learning algorithms only allow for supervised training settings. In this work, we proposed a novel federated learning framework, MixFedGAN, to tackle the above issues in federated networks with dynamic aggregation and knowledge distillation. A dynamic aggregation scheme was designed to reduce the impact of current low-performing clients and improve stability. Knowledge distillation was introduced into the local generator model with a new distillation regularization loss function to prevent essential parameters of the global generator model from significantly changing. In addition, we considered two scenarios under this framework: complete annotated data and limited labeled data. An experimental analysis on four heterogeneous COVID-19 infection segmentation datasets and three heterogeneous prostate MRI segmentation datasets verified the effectiveness of the proposed federated learning method.

Federated learning (FL) is a distributed model training paradigm that preserves clients’ data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose, an automatic FL hyper-parameter tuning algorithm tailored to applications’ diverse system requirements in FL training. iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of for diverse applications and FL aggregation algorithms, we show that is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of is available at.