Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributions
The rapid growth of data from edge devices has catalyzed the performance of machine
learning algorithms. However, the data generated resides at client devices thus there are …
learning algorithms. However, the data generated resides at client devices thus there are …
MFC: A Multishot Approach to Federated Data Clustering
The work explores the federated data clustering problem. The primary goal is to perform k-
means clustering of data distributed over multiple clients while preserving privacy during an …
means clustering of data distributed over multiple clients while preserving privacy during an …
Reducing Non-IID Effects in Federated Autonomous Driving with Contrastive Divergence Loss
Federated learning has been widely applied in autonomous driving since it enables training
a learning model among vehicles without sharing users' data. However, data from …
a learning model among vehicles without sharing users' data. However, data from …
[PDF][PDF] Addressing Non-IID Problem in Federated Autonomous Driving with Contrastive Divergence Loss
Federated learning has been widely applied in autonomous driving since it enables training
a learning model among vehicles without sharing users' data. However, data from …
a learning model among vehicles without sharing users' data. However, data from …