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 (FL) is a privacy-friendly type of machine learning wheredevices locally train a model on their private data and typically communicatemodel updates with a server. In decentralized FL (DFL), peers communicate modelupdates with each other instead. However, DFL is challenging since (1) thetraining data possessed by different peers is often non-i.i.d. (i.e.,distributed differently between the peers) and (2) malicious, or Byzantine,attackers can share arbitrary model updates with other peers to subvert thetraining process.  We address these two challenges and present Bristle, middleware between thelearning application and the decentralized network layer. Bristle leveragestransfer learning to predetermine and freeze the non-output layers of a neuralnetwork, significantly speeding up model training and lowering communicationcosts. To securely update the output layer with model updates from other peers,we design a fast distance-based prioritizer and a novel performance-basedintegrator. Their combined effect results in high resilience to Byzantineattackers and the ability to handle non-i.i.d. classes.  We empirically show that Bristle converges to a consistent 95% accuracy inByzantine environments, outperforming all evaluated baselines. In non-Byzantineenvironments, Bristle requires 83% fewer iterations to achieve 90% accuracycompared to state-of-the-art methods. We show that when the training classesare non-i.i.d., Bristle significantly outperforms the accuracy of the mostByzantine-resilient baselines by 2.3x while reducing communication costs by90%.

Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments