English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Federated learning is a distributed mechanism that trained large-scale neuralnetwork models with the participation of multiple clients and data remains ontheir devices, only sharing the local model updates. With this feature,federated learning is considered a secure solution for data privacy issues.However, the typical FL structure relies on the client-server, which leads tothe single-point-of-failure (SPoF) attack, and the random selection of clientsfor model training compromised the model accuracy. Furthermore, adversaries tryfor inference attacks i.e., attack on privacy leads to gradient leakageattacks. We proposed the blockchain-based optimized client selection andprivacy-preserved framework in this context. We designed the three kinds ofsmart contracts such as 1) registration of clients 2) forward bidding to selectoptimized clients for FL model training 3) payment settlement and reward smartcontracts. Moreover, fully homomorphic encryption with Cheon, Kim, Kim, andSong (CKKS) method is implemented before transmitting the local model updatesto the server. Finally, we evaluated our proposed method on the benchmarkdataset and compared it with state-of-the-art studies. Consequently, weachieved a higher accuracy rate and privacy-preserved FL framework withdecentralized nature.

Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning