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Duy-Dong Le on Muck Rack

Duy-Dong Le

As seen in: BioRxiv, PeerJ
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Articles

SelfDistillCore: a federated learning framework with sparse updates and historical knowledge integration

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By Duy-Dong Le, Tuong-Nguyen Huynh, Minh-Son Dao
| PeerJ
Federated learning (FL) has emerged as a transformative paradigm in distributed machine learning, enabling multiple clients to collaboratively train models without the need to share their raw data (McMahan et al., 2017).
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FedNolowe: A Normalized Loss-Based Weighted Aggregation Strategy for Robust Federated Learning in Heterogeneous Environments

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By Minh-Son Dao, Duy-Dong Le, Tuong-Nguyen Huynh, Anh-Khoa Tran
| BioRxiv
Abstract Federated Learning supports collaborative model training across distributed clients while keeping sensitive data decentralized. Still, non-independent and identically distributed data pose challenges like unstable convergence and client drift. We propose Federated Normalized Loss-based Weighted Aggregation (FedNolowe), a new method that weights client contributions using normalized training losses, favoring those with lower losses to improve global model stability.
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