Privacy evaluations of split LLM systems lack standardisation, and the new LLM-Attacker framework addresses this by recording attacker conditions and allowing comparable, reproducible assessments. In a…

#LLMSecurity #PrivacyResearch #AIRedTeaming #SplitLearning
https://arxiv.org/abs/2610.22722

A pipelined split learning method optimizes model fragment placement and micro‑batch size across multi‑hop edge networks, cutting training latency, the study reports. Read more: https://getnews.me/pipelined-split-learning-reduces-latency-in-multi-hop-edge-networks/ #splitlearning #edgenetworks

Attention-based Double Compression (ADC) reduces data sent in split learning with Vision Transformers by merging similar activations and pruning tokens, while keeping accuracy comparable. https://getnews.me/attention-based-compression-reduces-split-learning-overhead/ #splitlearning #adc

ZORRO adds zero‑knowledge proofs and frequency‑domain checks to split‑learning clients, cutting backdoor success to under 6% while proof generation and verification finish in under ten seconds. https://getnews.me/zorro-introduces-zero-knowledge-defense-for-split-learning/ #zorro #splitlearning