arXiv:2603.20504v1 Announce Type: cross
Abstract: Modern cloud inference creates a two sided privacy problem where users reveal sensitive inputs to providers, while providers must execute proprietary model weights inside potentially leaky execution environments. Fully homomorphic encryption (FHE) offers cryptographic guarantees but remains prohibitively expensive for modern architectures. We argue that progress requires co-design where specializing FHE schemes/compilers for the static structure of inference circuits, while simultaneously constraining inference architectures to reduce dominant homomorphic cost drivers. We outline a meet in the middle agenda and concrete optimization targets on both axes.
Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning
arXiv:2603.25006v1 Announce Type: cross Abstract: Early detection of rice leaf diseases is critical, as rice is a staple crop supporting a substantial share of the