LAUSR

Resource-Efficient Federated Learning With Non-IID Data: An Auction Theoretic Approach

Sign Up to like & get
recommendations!
1 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 & get
recommendations!
2 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 & get
recommendations!
1 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

FEEL: Federated End-to-End Learning With Non-IID Data for Vehicular Ad Hoc Networks

Sign Up to like & get
recommendations!
2 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

Federated Learning with Taskonomy for Non-IID Data

Sign Up to like & get
recommendations!
1 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

Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data

Sign Up to like & get
recommendations!
1 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

Federated Learning with Non-IID Data in Wireless Networks

Sign Up to like & get
recommendations!
1 Published in 2021 at "IEEE Transactions on Wireless Communications"

DOI: 10.1109/twc.2021.3108197

Abstract: Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is… read more here.

Keywords: non iid; federated learning; iid data; learning non ... See more keywords

Optimizing Multi-Objective Federated Learning on Non-IID Data with Improved NSGA-III and Hierarchical Clustering

Sign Up to like & get
recommendations!
1 Published in 2022 at "Symmetry"

DOI: 10.3390/sym14051070

Abstract: Federated learning (FL) can tackle the problem of data silos of asymmetric information and privacy leakage; however, it still has shortcomings, such as data heterogeneity, high communication cost and uneven distribution of performance. To overcome… read more here.

Keywords: nsga iii; multi objective; hierarchical clustering; non iid ... See more keywords