Optimal Model Transfer and Dynamic Parameter Server Selection for Efficient Federated Learning in IoT-Edge Systems With Non-IID Data
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3487073
Abstract: Distributed and privacy-preserving federated learning (FL) has been associated with edge computing systems for developing intelligent IoT applications. However, collecting data individually in each FL node may result in non-independent and identically distributed (non-IID) training… read more here.
Keywords: non iid; model; federated learning; model transfer ... See more keywords
Strategy-Switch: From All-Reduce to Parameter Server for Faster Efficient Training
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3528248
Abstract: Deep learning plays a pivotal role in numerous big data applications by enhancing the accuracy of models. However, the abundance of available data presents a challenge when training neural networks on a single node. Consequently,… read more here.
Keywords: strategy switch; parameter; training; reduce parameter ... See more keywords
PS+: A Simple yet Effective Framework for Fast Training on Parameter Server
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2022.3200518
Abstract: In distributed training, workers collaboratively refine the global model parameters by pushing their updates to the Parameter Server and pulling fresher parameters for the next iteration. This introduces high communication costs for training at scale,… read more here.
Keywords: parameter server; yet effective; framework; effective framework ... See more keywords