TY - CPAPER
AU - Felix Freitag
AU - Lu Wei
AU - Chun-Hung Liu
AU - Mennan Selimi
AU - Luís Veiga
AB - In federated learning, distributed nodes train a local machine learning model and exchange it through a central aggregator. In real environments, these training nodes are heterogeneous in computing capacity and bandwidth, thus their specific characteristics influence the performance of the federated learning process. We propose for such situations the design of a federated learning server that is able to adapt dynamically to the heterogeneity of the training nodes. In experiments with real devices deployed in a wireless mesh network, we observed that the designed adaptive federated learning server successfully exploited the idle times of the fast nodes by assigning them larger training workloads, which led to a higher global model performance without increasing the training time.
CY - Cusco, Peru
DA - 08/2023
N2 - In federated learning, distributed nodes train a local machine learning model and exchange it through a central aggregator. In real environments, these training nodes are heterogeneous in computing capacity and bandwidth, thus their specific characteristics influence the performance of the federated learning process. We propose for such situations the design of a federated learning server that is able to adapt dynamically to the heterogeneity of the training nodes. In experiments with real devices deployed in a wireless mesh network, we observed that the designed adaptive federated learning server successfully exploited the idle times of the fast nodes by assigning them larger training workloads, which led to a higher global model performance without increasing the training time.
PB - Springer International Publishing
PP - Cusco, Peru
PY - 2023
SN - 978-3-031-33261-6
TI - Server-side Adaptive Federated Learning over Wireless Mesh Network
UR - https://doi.org/10.1007/978-3-031-33261-6_25
ER -