Search Results for author:
Found 7 papers, 1 papers with code
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
Existing defenses against poisoning attacks in FL have several limitations, such as relying on specific assumptions about attack types and strategies or data distributions or not sufficiently robust against advanced injection techniques and strategies and simultaneously maintaining the utility of the aggregated model.
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks
Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning model without having them share their private data.
ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks
ARGUS monitors the contextual setting based on the state and actions of IoT devices in the environment.
BayBFed: Bayesian Backdoor Defense for Federated Learning
However, as these approaches directly operate on client updates, their effectiveness depends on factors such as clients' data distribution or the adversary's attack strategies.
CrowdGuard: Federated Backdoor Detection in Federated Learning
However, FL is susceptible to backdoor (or targeted poisoning) attacks.
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection
Using these techniques, DeepSight can identify suspicious model updates.
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS
Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates.
