Integrating Personalized Federated Learning with Control Systems for Enhanced Performance
In the expanding field of machine learning, federated learning has emerged as a pivotal methodology for distributed data environments, ensuring privacy while leveraging decentralized data sources. However, the heterogeneity of client data and the need for tailored models necessitate the integration of personalization techniques to enhance learning efficacy and model performance. This paper introduces a novel framework that amalgamates personalized federated learning with robust control systems, aimed at optimizing both the learning process and the control of data flow across diverse networked environments. Our approach harnesses personalized algorithms that adapt to the unique characteristics of each client's data, thereby improving the relevance and accuracy of the model for individual nodes without compromising the overall system performance. To manage and control the learning process across the network, we employ a sophisticated control system that dynamically adjusts the parameters based on real-time feedback and system states, ensuring stability and efficiency. Through rigorous experimentation, we demonstrate that our integrated system not only outperforms standard federated learning models in terms of accuracy and learning speed but also maintains system integrity and robustness in face of varying network conditions and data distributions. The experimental results, obtained from a multi-client simulated environment with non-IID data distributions, underscore the benefits of integrating control systems into personalized federated learning frameworks, particularly in scenarios demanding high reliability and precision.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningPersonalized Federated LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems
Personalized medication aims to tailor healthcare to individual patient characteristics. However, the heterogeneity of patient data across healthcare systems presents significant challenges to achieving accurate and effe…
Federated LearningMeta-LearningTransfer LearningAnalytic Personalized Federated Meta-Learning
Analytic Federated Learning (AFL) is an enhanced gradient-free federated learning (FL) paradigm designed to accelerate training by updating the global model in a single step with closed-form least-square (LS) solutions. …
Federated LearningMeta-LearningExploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos
The challenge of detecting violent incidents in urban surveillance systems is compounded by the voluminous and diverse nature of video data. This paper presents a targeted approach using Personalized Federated Learning (…
Federated LearningPersonalized Federated LearningPrivacy PreservingBlockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare
Federated learning (FL) has attracted increasing attention to mitigate security and privacy challenges in traditional cloud-centric machine learning models specifically in healthcare ecosystems. FL methodologies enable t…
Federated LearningPrivacy PreservingPersonalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
Nowadays, deep learning methods with large-scale datasets can produce clinically useful models for computer-aided diagnosis. However, the privacy and ethical concerns are increasingly critical, which make it difficult to…
Federated Learning