FedLC: Optimizing Federated Learning in Non-IID Data via Label-Wise Clustering
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3271517
Abstract: As contemporary systems are being operated in dynamic situations alternating into decentralized and distributed environments from conventional centralized frameworks, Federated Learning (FL) has been gaining attention for an effective architecture when aggregating the information and… read more here.
Keywords: non iid; federated learning; wise clustering; label wise ... See more keywords
Resource-Efficient Federated Learning With Non-IID Data: An Auction Theoretic Approach
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3197317
Abstract: Federated learning (FL) has gained significant importance for intelligent applications, following data produced on a massive scale by numerous distributed IoT devices. From an FL perspective, the key aspect is that this data is not… read more here.
Keywords: resource efficient; non iid; iid data; auction ... See more keywords
Clustered Federated Multitask Learning on Non-IID Data With Enhanced Privacy
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3228893
Abstract: Federated learning is a machine learning prgadigm that enables the collaborative learning among clients while keeping the privacy of clients’ data. Federated multitask learning (FMTL) deals with the statistic challenge of non-independent and identically distributed… read more here.
Keywords: non iid; multitask learning; iid data; privacy ... See more keywords
Adaptive Federated Learning on Non-IID Data with Resource Constraint
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Computers"
DOI: 10.1109/tc.2021.3099723
Abstract: Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without exposing their own data. One of the key challenges in FL is the… read more here.
Keywords: non iid; federated learning; iid data; model ... See more keywords
Federated Fuzzy Neural Network With Evolutionary Rule Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Fuzzy Systems"
DOI: 10.1109/tfuzz.2022.3207607
Abstract: Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local… read more here.
Keywords: neural network; rule; federated fuzzy; fuzzy neural ... See more keywords
Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise Modeling
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3175535
Abstract: Random noise attenuation is a key step in seismic field data processing. With the rise of artificial intelligence, deep learning (DL) algorithms are gradually introduced into seismic random noise suppression. The most commonly used DL… read more here.
Keywords: noise; non iid; random noise; gaussian noise ... See more keywords
FEEL: Federated End-to-End Learning With Non-IID Data for Vehicular Ad Hoc Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2022.3190294
Abstract: Recent studies have demonstrated the potentials of federated learning (FL) in achieving cooperative and privacy-preserving data analytics. It would also be promising if FL can be employed in vehicular ad hoc networks (VANETs) for cooperative… read more here.
Keywords: non iid; federated end; hoc networks; iid data ... See more keywords
A New Framework of Swarm Learning Consolidating Knowledge from Multi-Center Non-IID Data for Medical Image Segmentation.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on medical imaging"
DOI: 10.1109/tmi.2022.3220750
Abstract: Large training datasets are important for deep learning-based methods. For medical image segmentation, it could be however difficult to obtain large number of labeled training images solely from one center. Distributed learning, such as swarm… read more here.
Keywords: swarm learning; non iid; center; multi ... See more keywords
Federated Learning with Taskonomy for Non-IID Data
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3152581
Abstract: Classical federated learning approaches incur significant performance degradation in the presence of non-independent and identically distributed (non-IID) client data. A possible direction to address this issue is forming clusters of clients with roughly IID data.… read more here.
Keywords: non iid; federated learning; learning taskonomy; taskonomy non ... See more keywords
Non-IID Federated Learning With Sharper Risk Bound.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3213187
Abstract: In federated learning (FL), the not independently or identically distributed (non-IID) data partitioning impairs the performance of the global model, which is a severe problem to be solved. Despite the extensive literature related to the… read more here.
Keywords: local rademacher; non iid; federated learning; risk ... See more keywords
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2022.3150579
Abstract: Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considering heterogeneous computing and communication capabilities of different participants, asynchronous FL can avoid… read more here.
Keywords: non iid; iid data; federated learning; stale gradients ... See more keywords