MeditCom Best Journal Paper Award

ArtifiTialLeap has been awarded the Best Paper Award at IEEE MeditCom 2022 for its research on reducing communication costs in Federated Learning. The winning paper, "Sine Cosine Algorithm for Reducing Communication Costs of Federated Learning," was authored by Ammar Kamal Abasi, Moayad Aloqaily, Mohsen Guizani, and Fakhri Karray.

Federated Learning (FL) is a Machine Learning (ML) setting in which several clients train a model cooperatively under the direction of a central server while training data is decentralized. However, communication among clients and servers can be challenging, particularly in Unstable Network Environments (UNE), where existing FL aggregation techniques send and receive many weights, dramatically reducing accuracy.

Federated Learning (FL) is a Machine Learning (ML) setting in which several clients train a model cooperatively under the direction of a central server while training data is decentralized. However, communication among clients and servers can be challenging, particularly in Unstable Network Environments (UNE), where existing FL aggregation techniques send and receive many weights, dramatically reducing accuracy.

The IEEE MeditCom Best Paper Award is a significant recognition that acknowledges exceptional contributions in the communications field. ArtifiTialLeap's research has significantly impacted the field of Federated Learning, and its innovative approach can potentially improve communication efficiency and accuracy. Their achievement is a testament to their commitment to advancing research in the communications field and their dedication to producing high-quality work.

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