arXiv AI July 28, 2026 By Jianhang Xie, Sicheng Tan, Vishnu Naresh Boddeti, Zhichao Lu

ATLAS: Automated Approximation of Transformers for Efficient Homomorphic Inference in One Hour

arXiv:2607. 23478v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) provides strong cryptographic guarantees for private inference, but deploying transformer models under FHE remains prohibitively expensive.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Sep 3

HEAT: Faster Fully Homomorphic Inference via Approximations-Weights Co-Adaptation

HEAT introduces a fine‑tuning method that treats the number of iterations used to approximate nonlinearities in fully homomorphic encryption (FHE) as learnable parameters, allowing them to co‑adapt with model weights. By optimizing iteration counts per nonlinearity, HEAT reduces the required iterations, bootstraps, and overall latency for encrypted GPT‑2 decoding while improving decode agreement. The approach achieves a 3.1× reduction in iterations, a 1.6× reduction in bootstraps, and a 1.4× speed‑up in end‑to‑end latency without changing the model architecture or requiring retraining from scratch.

By Alessandro Zirilli, Davide Marincione, Evgenios M. Kornaropoulos, Giuseppe Ateniese, Emanuele Rodol\`a

llms fine-tuning

arXiv Machine Learning

Jul 7

Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers

arXiv:2607. 04819v1 Announce Type: new Abstract: Fully homomorphic encryption (FHE) enables computation on encrypted data, but practical encrypted Transformer inference is bottlenecked by the sequential composition of many nonlinear blocks.

By Ligong Han, Kai Xu, Hao Wang, Ruijiang Gao, Akash Srivastava

llms

arXiv AI

Aug 3

MOSAIC: Masked Outsourcing of Secure AI Computations

arXiv:2607. 29221v1 Announce Type: cross Abstract: We address the challenge of securely and efficiently outsourcing AI computations from a trusted but computationally weak client to an untrusted but powerful server, in the setting where the client holds both the input and the model, and the server must learn neither.

By James Hsin-yu Chiang, Sheila Zingg, Kari Kostiainen, Srdjan Capkun

llms efficiency benchmarks

arXiv Machine Learning

Jun 16

FEnc$^2$: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding

arXiv:2606. 16359v1 Announce Type: cross Abstract: Fully Homomorphic Encryption (FHE) enables privacy-preserving machine learning but incurs extreme computational and memory overhead.

By Ran Ran, Zhaoting Gong, Nuo Xu, Yuanchao Xu, Fan Yao, Wujie Wen

benchmarks safety

arXiv Machine Learning

4d ago

CipherGenome: Homomorphic Inference for Genomic Mixture-of-Experts

arXiv:2609.35883v1 Announce Type: cross Abstract: Genome foundation models are growing into sparse mixture-of-experts (MoE) networks whose expert weights no longer fit on the machines that hold the s...

By Guang Yang, Fengchen Liu

rag

arXiv AI

Aug 10

Fast LapSum: Exact Differentiable Top-k at Million Scale

arXiv:2608. 06912v1 Announce Type: new Abstract: The top-$k$ operation is a fundamental building block of modern sparse computation, enabling token routing, expert activation, memory selection, and attention pruning.

By {\L}ukasz Struski, Joanna Wojciechowicz, Jakub Antczak, Marcin Mazur, Kamil Ksi\k{a}\.zek, Jacek Tabor

efficiency benchmarks safety

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Related stories

HEAT: Faster Fully Homomorphic Inference via Approximations-Weights Co-Adaptation

Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers

MOSAIC: Masked Outsourcing of Secure AI Computations

FEnc$^2$: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding

CipherGenome: Homomorphic Inference for Genomic Mixture-of-Experts

Fast LapSum: Exact Differentiable Top-k at Million Scale