Cover Image

Time-Distributed Backdoor Attacks on Federated Spiking Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Abad, Gorka, Picek, Stjepan, Urbieta, Aitor
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security Computer Vision and Pattern Recognition Neural and Evolutionary Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data
by: Abad, Gorka, et al.
Published: (2023)

Removing the Trigger, Not the Backdoor: Alternative Triggers and Latent Backdoors
by: Abad, Gorka, et al.
Published: (2026)

A Systematic Evaluation of Evolving Highly Nonlinear Boolean Functions in Odd Sizes
by: Carlet, Claude, et al.
Published: (2024)

Degree is Important: On Evolving Homogeneous Boolean Functions
by: Carlet, Claude, et al.
Published: (2025)

IDEM Enough? Evolving Highly Nonlinear Idempotent Boolean Functions
by: Carlet, Claude, et al.
Published: (2026)

A Systematic Study on the Design of Odd-Sized Highly Nonlinear Boolean Functions via Evolutionary Algorithms
by: Carlet, Claude, et al.
Published: (2025)

Context is the Key: Backdoor Attacks for In-Context Learning with Vision Transformers
by: Abad, Gorka, et al.
Published: (2024)

Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks
by: Jin, Lingxin, et al.
Published: (2024)

Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras
by: Riaño, Roberto, et al.
Published: (2024)

Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
by: Xu, Nuo, et al.
Published: (2022)

Monotone but Exciting: On Evolving Monotone Boolean Functions with High Nonlinearity
by: Carlet, Claude, et al.
Published: (2026)

NegaBent, No Regrets: Evolving Spectrally Flat Boolean Functions
by: Carlet, Claude, et al.
Published: (2026)

On Counts and Densities of Homogeneous Bent Functions: An Evolutionary Approach
by: Carlet, Claude, et al.
Published: (2025)

Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition
by: Yang, Siyuan, et al.
Published: (2025)

HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
by: Geng, Hejia, et al.
Published: (2023)

Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metrics
by: Brama, Haya, et al.
Published: (2022)

Enhancing Adversarial Robustness in SNNs with Sparse Gradients
by: Liu, Yujia, et al.
Published: (2024)

A Theoretical View of Linear Backpropagation and Its Convergence
by: Li, Ziang, et al.
Published: (2021)

Adversarial Robustness on Image Classification with $k$-means
by: Omari, Rollin, et al.
Published: (2023)

Adversarial Example Generation using Evolutionary Multi-objective Optimization
by: Suzuki, Takahiro, et al.
Published: (2019)

Protecting Feed-Forward Networks from Adversarial Attacks Using Predictive Coding
by: Ganjidoost, Ehsan, et al.
Published: (2024)

Backdoor Directions in Vision Transformers
by: Karayalcin, Sengim, et al.
Published: (2026)

A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks
by: Nikfam, Farzad, et al.
Published: (2023)

Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks
by: Poursiami, Hamed, et al.
Published: (2025)

Hybrid Deep Learning Model for Multiple Cache Side Channel Attacks Detection: A Comparative Analysis
by: Joshi, Tejal, et al.
Published: (2025)

Privacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms
by: Gruber, Sebastian, et al.
Published: (2026)

On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning
by: Lin, Jiaqi, et al.
Published: (2025)

Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation
by: Kang, Donghwa, et al.
Published: (2025)

LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm
by: Liu, Dazhuang, et al.
Published: (2024)

CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors
by: Yang, Guangmingmei, et al.
Published: (2024)

Adversarially Robust Spiking Neural Networks with Sparse Connectivity
by: Schmolli, Mathias, et al.
Published: (2025)

Event-Driven Learning for Spiking Neural Networks
by: Wei, Wenjie, et al.
Published: (2024)

Watermarking Neuromorphic Brains: Intellectual Property Protection in Spiking Neural Networks
by: Poursiami, Hamed, et al.
Published: (2024)

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields
by: Lin, Ranxi, et al.
Published: (2025)

Spiking Diffusion Models
by: Cao, Jiahang, et al.
Published: (2024)

Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition
by: El-Assal, Mireille, et al.
Published: (2023)

Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
by: Fang, Wei, et al.
Published: (2023)

MALT Powers Up Adversarial Attacks
by: Melamed, Odelia, et al.
Published: (2024)

A Discrete Particle Swarm Optimizer for the Design of Cryptographic Boolean Functions
by: Mariot, Luca, et al.
Published: (2024)

Proof-of-Useful-Work Blockchain for Trustworthy Biomedical Hyperdimensional Computing
by: Wen, Jinghao, et al.
Published: (2022)