privacy-preserving-machine-learning

topic14 events

papersTODAY 04:00 UTC

LLM-based split learning predicts mental distress across heterogeneous surveys

A new arXiv paper proposes a schema-aware split learning approach that uses LLMs to predict mental distress from survey data while keeping sensitive records private. The method is designed to work across surveys with differing structures and questions, which is a common obstacle when pooling mental health data from schools, employers, and clinics. The work targets privacy-preserving collaboration, so data stays local rather than being centralized.

privacy/split-learning/mental-health/llm

papersTODAY 04:00 UTC

arXiv paper proposes privacy-preserving gossip learning with sequential updates

A new arXiv preprint describes a decentralized learning setup where each agent keeps one private data sample alongside a shared model, and samples are processed one after another. Each update is designed to keep the model's predictions at already-learned samples unchanged, which the authors present as a way to limit forgetting while protecting privacy. The work sits at the intersection of gossip-style distributed training and privacy-preserving machine learning.

privacy/decentralized/federated-learning/arxiv

papersTODAY 04:00 UTC

Compact Adaptation Method Aligns Personalized Federated Learning with Record-Level Privacy

A new arXiv paper examines how record-level differential privacy clashes with personalized federated learning, where each client's individual variation is low-dimensional but repeated training rounds broadcast high-dimensional model updates. The authors propose a compact adaptation approach paired with variable-length Gaussian communication to reduce that mismatch. The work targets more efficient and privacy-preserving personalization across federated clients.

federated-learning/differential-privacy/research/personalization

papersTODAY 04:00 UTC

Differential Privacy of Gaussian Process Posterior Sampling

This paper studies the privacy guarantees of releasing posterior sample paths from a Gaussian process when the entire training set, including covariates and responses, is considered private. Rather than relying on standard differential-privacy mechanisms that add external noise, the analysis focuses on the randomness inherent in posterior sampling itself. It provides a formal treatment of how much privacy such released sample paths preserve.

differential-privacy/gaussian-processes/privacy/research

papersTODAY 04:00 UTC

CANAL adds channel-aware noise allocation for private medical image segmentation

A new arXiv paper proposes CANAL, a method that lets hospitals train segmentation models together without sharing patient scans. It uses knowledge distillation to transfer learned feature representations, injecting differential-privacy noise in a channel-aware way so that the privacy budget is spent where it matters most. The approach targets medical image segmentation, where complementary data sits in separate institutions that cannot legally exchange it.

differential-privacy/medical-imaging/knowledge-distillation/federated-learning

papersTODAY 04:00 UTC

Federated Learning Framework Combines Differential Privacy and Homomorphic Encryption

A new arXiv paper presents a federated learning framework designed to make collaborative training across distributed data safer. It combines dynamic differential privacy, a lightweight homomorphic encryption scheme, and asynchronous aggregation to reduce privacy risks while limiting computational overhead. The work is posted as a preprint and has not yet been peer reviewed.

federated-learning/privacy/differential-privacy/encryption

papersSEP 11 04:00 UTC

mmFHE Runs Whole mmWave Sensing Pipeline Under Homomorphic Encryption

A new arXiv paper introduces mmFHE, a system that performs an entire cloud-side mmWave sensing workflow, including signal processing and machine learning inference, on encrypted data using fully homomorphic encryption. Range profiles are encrypted on the edge device first, so the cloud never sees raw sensing data. The work aims to enable privacy-preserving sensing services without giving up cloud compute.

privacy/homomorphic-encryption/mmwave/research

papersSEP 11 04:00 UTC

Federated Learning Challenge Reports Results for Surgical Appendicitis Classification

A paper summarizes the FedSurg EndoVis 2024 Challenge, which tested federated learning methods on surgical video for appendicitis classification without centralizing patient data. The work addresses the difficulty of building generalizable surgical AI when hospitals cannot share video directly, and reports benchmark outcomes from participating teams. It positions federated training as a viable approach for privacy-sensitive, spatiotemporal surgical tasks.

federated-learning/medical-ai/benchmark/computer-vision

papersSEP 11 04:00 UTC

Adaptive Diffusion Freezing proposed against membership inference attacks

A new arXiv preprint introduces a technique called Adaptive Diffusion Freezing that aims to make diffusion models more resistant to membership inference attacks, which try to determine whether a specific sample was part of the training data. The method adapts how parts of the model are frozen during training to limit the privacy leakage that standard diffusion training can expose.

privacy/diffusion-models/membership-inference/research

papersSEP 10 04:00 UTC

Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G

A new research paper proposes a federated learning framework that separates modality-specific processing so heterogeneous robots in 6G networks can train collaboratively without sharing raw sensor data. The approach targets privacy preservation for embodied AI applications built on low-latency edge connectivity and distributed sensing.

federated-learning/robotics/6g/privacy

papersSEP 10 04:00 UTC

New Paper Studies Selective Homomorphic Inference for Efficient Private ML

A research paper on arXiv examines selective homomorphic inference, an approach to running machine learning on private data using fully homomorphic encryption. Instead of evaluating an entire input under encryption, which is computationally expensive, the method applies FHE only to the sensitive region of interest. The work aims to make privacy-preserving inference more efficient and practical.

privacy/homomorphic-encryption/machine-learning/research

papersSEP 10 04:00 UTC

arXiv paper proposes privacy-preserving split learning for federated LLM fine-tuning

A new arXiv paper addresses fine-tuning large language models in settings where participants cannot host the full model locally, often because the model owner keeps it proprietary. The authors introduce a split learning approach designed to preserve privacy during federated fine-tuning on domain-specific data.

llm/privacy/federated-learning/research

papersSEP 10 04:00 UTC

Researchers Propose Influence-Based Weighting for Personalized Federated Learning

A revised arXiv preprint introduces a personalized federated learning method that weights each client's parameter updates according to its influence, rather than relying on fixed aggregation weights. The approach is designed to let devices with different data distributions and preferences train collaboratively while keeping their data private. The updated version appears across arXiv's cs.AI and cs.LG listings.

federated-learning/privacy/personalization/machine-learning

papersSEP 10 04:00 UTC

NEXUS-MI: federated personalization framework for EEG motor-imagery brain-computer interfaces

Researchers present NEXUS-MI, an approach that personalizes EEG-based motor-imagery brain-computer interfaces through federated learning, letting user models improve without centralizing sensitive neural recordings. A gateway coordinates training in a communication-aware way, addressing variability across subjects and sessions. The goal is reliable adaptation when each person has only a small amount of calibration data.

federated-learning/bci/personalization/privacy