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Efficient Model Compression for Hierarchical Federated Learning
Federated learning (FL), as an emerging collaborative learning paradigm, has garnered significant attention due to its capacity to preserve privacy within distributed learning systems.
WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy
For data isolated islands and privacy issues, federated learning has been extensively invoking much interest since it allows clients to collaborate on training a global model using their local data without sharing any with a third party.
The Wilson-loop $d \log$ representation for Feynman integrals
We introduce and study the Wilson-loop ${\rm d}\log$ representation of certain Feynman integrals for scattering amplitudes in ${\cal N}=4$ SYM and beyond, which makes their evaluation completely straightforward.
High Energy Physics - Theory High Energy Physics - Phenomenology
