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Showing 1-25 of 81 resultsfor "Index Terms":personalized federated learning (PFL)
"Index Terms":personalized federated learning (PFL)
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PFL-DSSE: A Personalized Federated Learning Approach for Distribution System State Estimation
A centralized framework-based data-driven framework for active distribution system state estimation (DSSE) has been widely leveraged. However, it is challenged by potential data privacy breaches due to the aggregation of raw measurement data in a data center. A personalized federated learning-based DSSE method (PFL-DSSE) is proposed in a decentralized training framework for DSSE. Experimenta...Show More
Personalized Federated Learning via Deviation Tracking Representation Learning
2024 International Conference on Information Networking (ICOIN)
Year: 2024 | Conference Paper |
Federated learning preserves privacy by decentralized training of individual client devices, ensuring only model weights are shared centrally. However, the data heterogeneity across clients presents challenges. This paper focuses on representation learning, a variant of personalized federated learning. According to various studies, the representation learning model can be divided into two: the bas...Show More
Asynchronous Personalized Learning for Heterogeneous Wireless Networks
2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Year: 2023 | Conference Paper |
The future wireless networks are expected to support more artificial intelligence (AI)-enabled applications, such as Metaverse services, at the network edge. The AI algorithms, like deep learning, play an important role in extracting important information from a large dataset, but conventional centralized learning requires collecting the datasets that are distributed over the users and always incl...Show More
FedFTHA: A Fine-Tuning and Head Aggregation Method in Federated Learning
Yansong Wang;Hui Xu;Waqar Ali;Miaobo Li;Xiangmin Zhou;Jie Shao
IEEE Internet of Things Journal
Cited by: Papers (5)
Personalized federated learning (PFL) is a subfield of federated learning. Contrary to conventional federated learning that expects to find a general global model, PFL generates a personalized model adapted to its local data distribution for each client. Some existing PFL methods only consider improving the client-side personalization ability, discarding the server-side generaliz...Show More
Towards Personalized Federated Learning
In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI research, there has been growing awareness and concerns of data privacy. Recent significant developments in the data regulation landscape have prompted a seismic shift in interest toward privacy-preserving AI. This has contributed to the popularity of Federated Learning (FL), the leading paradigm for th...Show More
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism
Yiqing Shen;Baiyun Liu;Ruize Yu;Yudong Wang;Shaokang Wang;Jiangfen Wu;Weidao Chen
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide. Yet, COPD diagnosis heavily relies on spirometric examination as well as functional airway limitation, which may cause a considerable portion of COPD patients underdiagnosed especially at the early stage. Recent advance in deep learning (DL) has shown their promising potential in COPD identification from C...Show More
Personalized Federated Learning by Domain-Aware Network Pruning and Re-growth
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Federated learning (FL) is a machine learning paradigm where multiple clients train their local machine learning models collaboratively (without sharing private data). One of the main challenges in FL is statistical heterogeneity of the data distributions across clients. Personalized FL (PFL) mitigates statistical heterogeneity by collaborative model training across homogeneous clients. In t...Show More
Personalized Federated Learning Techniques: Empirical Analysis
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy. However, achieving optimal performance in pFL often requires a careful balancing act between memory overhead costs and model accuracy. This paper delves into the trade-offs inherent in pFL, offering valuable insights for selecting th...Show More
Clustering-Based Custom Global Modules for Personalized Federated Learning
Zining Chen;Can Wang;Baisong Zhang;Weiye Wang;Wen sheng Li;Wei dong Zhang
2024 Twelfth International Conference on Advanced Cloud and Big Data (CBD)
Year: 2024 | Conference Paper |
The field of federated learning faces a fundamental challenge posed by the non-independent and identically distributed (Non-IID) data among heterogeneous clients. Personalized federated learning (PFL) addresses this issue by providing customized models for each client. The existing PFL methods ignore the impact of redundant information brought by shared knowledge on processing local ta...Show More
Bayesian Optimisation-Driven Poisoning Attack Against Personalised Federated Learning in Metaverse
2024 IEEE International Conference on Communications Workshops (ICC Workshops)
Year: 2024 | Conference Paper |
Metaverse is envisioned to be a human-centric framework that aims to provide a comprehensive and immersive experience for users in various domains, including education, medicine, and entertainment. As a large amount of private data is generated from each user, federated learning (FL) has emerged as an effective solution to ensure user privacy. Moreover, personalized FL (PFL) was further stud...Show More
Enhancing Healthcare AI with Cross-Silo Personalized Federated Learning on Naturally Split Heterogeneous Data
2024 IEEE Region 10 Symposium (TENSYMP)
Year: 2024 | Conference Paper |
The potential of Artificial Intelligence (AI) in health-care is unavoidable. However, its success depends on the availabil-ity of large, high-quality datasets. Because of data heterogeneity across institutions and privacy concerns, traditional centralized Machine Learning (ML) approaches often face difficulties in this field. Federated Learning (FL) allows collaborative model training without requ...Show More
Personalized Federated Learning (PFL) has been extensively studied to overcome the Non-IID challenges of FL. However, given realistic and non-pathologically Non-IID distributions, the elaborate personalized models can even be inferior to a naive single global model. In this paper, we take the first step toward further understanding when PFL is better on a common Non-IID distribution: l...Show More
Enhancing Both Generalization and Personalization in Personalized Federated Learning via Global and Local Knowledge Distillation
2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)
Year: 2024 | Conference Paper |
Federated Learning (FL) is a distributed machine learning framework that incorporates privacy protection. Under the coordination of a central server, clients collaboratively train a global model without exposing their raw data. A significant challenge in traditional FL is data heterogeneity, which can degrade the performance of the global model. To address this issue, Personalized Federated Learni...Show More
Federated Learning-Based Anomaly Classification Detection for Solar Photovoltaic Panels
2024 9th International Conference on Electronic Technology and Information Science (ICETIS)
Year: 2024 | Conference Paper |
The solar photovoltaic (PV) industry necessitates robust anomaly detection mechanisms to ensure the efficiency and longevity of solar panels. However, conventional anomaly detection approaches encounter significant hurdles related to data privacy and management. To overcome these challenges, we propose a federated learning (FL) framework that facilitates collaborative model development while prese...Show More
Personalized Federated Learning with Kolmogorov-Arnold Enhanced CNN for Rotating Equipment Fault Diagnosis
Delanyo Kwame Bensah Kulevome;Qiu Man;Ernest Smith Mawuli;Edward Opoku-Mensah
2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)
Year: 2024 | Conference Paper |
Fault diagnosis of rotating equipment plays a crucial role in ensuring operational reliability and minimizing downtime in industrial systems. This study proposes a novel approach that integrates personalized federated learning (PFL) with a Kolmogorov-Arnold network (KAN)-enhanced convolutional neural network (CNN) to improve diagnostic accuracy across heterogeneous operational environments. ...Show More
Bilateral Improvement in Local Personalization and Global Generalization in Federated Learning
Federated learning (FL) is a machine learning paradigm where a server trains a global model by amalgamating contributions from multiple clients, without accessing personal client data directly. Personalized FL (PFL), a specific subset of this domain, shifts focus from a global model to providing personalized models for each client. This difference in training objectives signifies that while ...Show More
Predictive maintenance (PdM) has entered into a new era adopting artificial intelligence and Internet-of-Things (IoT) technologies. It is necessary for a manufacturing company to collaborate with other clients using IoT-captured production data. However, training models in a cross-silo manner is still challenging when considering data privacy. In order to tackle these challenges, a personalized cr...Show More
Community Awareness Personalized Federated Learning for Defect Detection
Hongwei Zhao;Qiyuan Liu;Haoyun Sun;Liang Xu;Weishan Zhang;Yikang Zhao;Fei-Yue Wang
Multiple organizations in social manufacturing can collaborate on high-quality product defect detection with social networks. Federated learning (FL) is an emerging paradigm where multiple clients can collaboratively train a defect detection model in a privacy-preserving manner. A prevalent issue in FL, concept drift, is discussed in this article. Feature representations of the same label may vary...Show More
PR-PFL: A Privacy-Preserving and Robust Personalized Federated Learning Framework
Ruiguang Yang;Xiaodong Shen;Chang Xu;Liehuang Zhu;Kashif Sharif
2024 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)
Year: 2024 | Conference Paper |
Personalized Federated Learning (PFL) tackles the challenges of FL on heterogeneous data and provides customized solutions to each client. However, like commonly employed FL settings, PFL is still vulnerable to attacks on privacy and model availability. Existing PFL frameworks focus on either privacy protection or attack defense instead of simultaneously implementing both functio...Show More
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning
Brianna Mueller;W. Nick Street;Stephen Baek;Qihang Lin;Jingyi Yang;Yankun Huang
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. However, existing FL paradigms face challenges due to heterogeneity in client data distributions and system capabilities. Personalized federated learning (pFL) has been proposed to mitigate these problems, but often requires a shared model ...Show More
Adaptive Layered-Trust Robust Defense Mechanism for Personalized Federated Learning
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Personalized Federated Learning (PFL) is confronted with escalating security threats, yet existing defense strategies primarily concentrate on traditional federated learning, lacking robust defense mechanisms tailored for PFL. To fortify the robustness of PFL against stealthy malicious attacks, we propose an adaptive layered-trust robust defense mechanism, PFL-ALB. Firstly,...Show More
Prompt-Based Personalized Federated Learning for Medical Visual Question Answering
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns in traditional medical visual question answering (VQA) methods. Specifically, we regard medical datasets from different organs as clients and use pFL to train personalized transformer-based VQA models for each client. To address the high computational complexi...Show More
BS-pFL: Enabling Low-Cost Personalized Federated Learning by Exploring Weight Gradient Sparsity
2022 International Joint Conference on Neural Networks (IJCNN)
Year: 2022 | Conference Paper |
Recent advancements in Convolution Neural Networks (CNNs) have achieved amazing success in numerous applications. The record-breaking performance of CNNs is usually at the prohibitive training costs, thus all training data are usually processed at the powerful centralized server side, which rises privacy concerns. Federated learning (FL) is a distributed machine learning method over mobile devices...Show More
Dual-Objective Personalized Federated Service System With Partially-Labeled Data Over Wireless Networks
Federated learning (FL) emerges to mitigate the privacy concerns in machine learning-based services and applications, and personalized federated learning (PFL) evolves to alleviate the issue of data heterogeneity. However, FL and PFL usually rest on two assumptions: the users’ data is well-labeled, or the personalized goals align with sufficient local data. Unfortunately, the two assum...Show More
Communication-Efficient Personalized Federated Learning for Digital Twin in Heterogeneous Industrial IoT
2023 IEEE International Conference on Communications Workshops (ICC Workshops)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
As an up-coming digitalization technology, the digital twin (DT) offers a viable implementation for dynamic perception and intelligent decision-making in the industrial IoT (IIoT). For synchronizing the real-time information between the device and its DT, communication is fundamental to the digital twin system. Federated learning (FL) based DT model framework could be seen as an emerging paradigm ...Show More
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