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Federated learning (a way for multiple parties to train an AI model together without sharing their raw data with a central server) normally requires many communication rounds that waste bandwidth and can leak private information. Existing compression methods reduce communication but ignore privacy risks and fail when some clients disconnect. Octopus addresses these issues by using Sketch (a data compression technique) to compress gradients (the direction and size of updates to a model), adding protective masks around the compressed data, and including a strategy to handle disconnected clients.

Federated learning (a training method where multiple parties collaborate to build an AI model without sharing raw data) is vulnerable to model poisoning attacks (where attackers inject harmful updates during training to break the model). This paper proposes MSDFL and HMSDFL, new defensive approaches that strengthen models by improving their stability, meaning they become less sensitive to small changes in their internal parameters, making them more resistant to these poisoning attacks.

EEG-FE_rrRS is a biometric recognition system that uses brain wave signals (EEG, electroencephalogram) and a fuzzy extractor (a cryptographic tool that converts messy biometric data into secure, consistent digital codes) to create unique digital identities for users in applications like drones and virtual worlds. The system combines EEG signal processing with a fuzzy extractor framework and demonstrates high accuracy in recognizing individuals, achieving nearly perfect results on certain datasets.

Millimeter-wave radars (mmWave, sensors that use radio waves to detect objects) used in autonomous vehicles can be tricked by attackers who send false signals to distort what the radar perceives, potentially causing dangerous driving behavior. AttackDeceiver is a new anti-spoofing system (a defense against false signal attacks) that uses a phase-shifted interleaving waveform (a specially designed radio signal pattern) to detect fake targets by comparing readings from two independent channels, and it also tricks adaptive attackers into creating unrealistic fake objects that are easier to identify.

Researchers demonstrated a flow correlation attack against Nym, a mixnet (a network system that hides which user is communicating with which destination by routing traffic through multiple nodes). By analyzing the pattern and rate of data packets, an attacker controlling entry and exit gateways can use a neural network (a machine learning model inspired by how brains process information) to match incoming flows with outgoing flows with very high accuracy. The study tested five defense strategies and found that using the right combination of countermeasures at appropriate scales can meaningfully reduce the attack's effectiveness.

PrivESD is a new system that allows machine learning classification (logistic regression, a technique for categorizing data) to work on encrypted streaming data (continuously flowing information that's been scrambled for privacy) while stored in the cloud. The system splits the computational work between cloud servers and edge devices (computers closer to where data originates) to reduce processing burden and privacy risks, and uses special encryption methods that still allow the system to compare values without revealing the actual data.

Researchers discovered that hyper-parameters (settings that control how a deep reinforcement learning model learns and behaves) can be leaked from closed-box DRL models, meaning attackers can figure out these secret settings just by observing how the model responds to different situations. They created an attack called HyperInfer that successfully inferred hyper-parameters with over 90% accuracy, showing that even restricted AI models may expose information that was meant to stay hidden.

Researchers created a method called UTE-SS (Unlearnable text examples generation via syntax-oriented shortcut) to protect text data from being used to train AI models without permission. The method adds small, hard-to-notice changes to text by altering its syntax (grammatical structure) so that language models learn misleading patterns instead of useful information, making the text data effectively useless for training.

This paper presents a new system for 3-D multiobject tracking (MOT, a technique where AI follows multiple objects moving through 3-D space) used in autonomous vehicles to improve safety. The system uses a voxel masking encoder (a method that processes 3-D space divided into small cubes, focusing on important features while ignoring empty space) and deep hashing (a technique that converts objects into compact numerical codes for fast comparison) to better track distant objects, partially hidden objects, and similar-looking objects. The method was tested on the KITTI dataset (a standard collection of driving videos used to evaluate autonomous vehicle systems) and showed better tracking accuracy than existing methods.

FedMPS is a federated learning (FL, a technique where multiple computers train an AI model together without sharing raw data) framework that addresses performance problems caused by data heterogeneity (differences in data across participants). Instead of exchanging full model parameters, FedMPS transmits only prototypes (representative feature patterns) and soft labels (probability-based output predictions), which reduces communication costs and improves how well models learn from each other.

Hard sample mining (HSM, a technique for selecting the most difficult training examples to focus a model's learning) has emerged as a method to improve how efficiently deep neural networks (AI systems based on interconnected layers inspired by brain neurons) train and make them more robust to errors. This survey article reviews different HSM approaches and explains how they help address training inefficiency and data distribution biases (when training data doesn't represent real-world scenarios fairly) in deep learning.