Sökning: "hierarkisk federerad inlärning"

Hittade 2 uppsatser innehållade orden hierarkisk federerad inlärning.

1. 1. Cluster selection for Clustered Federated Learning using Min-wise Independent Permutations and Word Embeddings

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Pulasthi Raveen Bandara Harasgama; [2022]
   Nyckelord :Federated learning; Distributed machine learning; Clustering; Word Embeddings; Federerad inlärning; Distribuerad maskininlärning; Klustring; Ordinbäddningar;

   Sammanfattning : Federated learning is a widely established modern machine learning methodology where training is done directly on the client device with local client data and the local training results are shared to compute a global model. Federated learning emerged as a result of data ownership and the privacy concerns of traditional machine learning methodologies where data is collected and trained at a central location. LÄS MER

3. 2. Federated Learning in Large Scale Networks : Exploring Hierarchical Federated Learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Henrik Eriksson; [2020]
   Nyckelord :Federated learning; personalization; time series forecasting; clustering; hierarchical federated learning; model interpolation; Mapper; HierFAvg; base stations; non-IID; LSTM; Federerad inlärning; tidsserieprognostisering; personalisering; kluster; hierarkisk federerad inlärning; modellinterpolation; Mapper; HierFAvg; basstationer;

   Sammanfattning : Federated learning faces a challenge when dealing with highly heterogeneous data and it can sometimes be inadequate to adopt an approach where a single model is trained for usage at all nodes in the network. Different approaches have been investigated to succumb this issue such as adapting the trained model to each node and clustering the nodes in the network and train a different model for each cluster where the data is less heterogeneous. LÄS MER