On the Initial Behavior Monitoring Issues in Federated Learning

• URL: http://arxiv.org/abs/2109.05385v1
• Date: Sat, 11 Sep 2021 22:37:55 GMT
• Title: On the Initial Behavior Monitoring Issues in Federated Learning
• Authors: Ranwa Al Mallah, Godwin Badu-Marfo, Bilal Farooq
• Abstract summary: In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief. Some defenses are based on malicious worker detection and behavioral pattern analysis. We study the information inside the learning process in the early stages of training, propose a monitoring process and evaluate the monitoring period required.
• Score: 7.979659145328856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief. Regarding the cybersecurity of FL, some attacks aim at injecting the fabricated local model updates into the system. Some defenses are based on malicious worker detection and behavioral pattern analysis. In this context, without timely and dynamic monitoring methods, the chief cannot detect and remove the malicious or unreliable workers from the system. Our work emphasize the urgency to prepare the federated learning process for monitoring and eventually behavioral pattern analysis. We study the information inside the learning process in the early stages of training, propose a monitoring process and evaluate the monitoring period required. The aim is to analyse at what time is it appropriate to start the detection algorithm in order to remove the malicious or unreliable workers from the system and optimise the defense mechanism deployment. We tested our strategy on a behavioral pattern analysis defense applied to the FL process of different benchmark systems for text and image classification. Our results show that the monitoring process lowers false positives and false negatives and consequently increases system efficiency by enabling the distributed learning system to achieve better performance in the early stage of training.

Related papers

• Detecting Object Tracking Failure via Sequential Hypothesis Testing [80.7891291021747]
  Real-time online object tracking in videos constitutes a core task in computer vision.<n>We propose interpreting object tracking as a sequential hypothesis test, wherein evidence for or against tracking failures is gradually accumulated over time.<n>We propose both supervised and unsupervised variants by leveraging either ground-truth or solely internal tracking information.
  arXiv  Detail & Related papers  (2026-02-13T14:57:15Z)
• Algorithms for Adversarially Robust Deep Learning [58.656107500646364]
  We discuss recent progress toward designing algorithms that exhibit desirable robustness properties.<n>We present new algorithms that achieve state-of-the-art generalization in medical imaging, molecular identification, and image classification.<n>We propose new attacks and defenses, which represent the frontier of progress toward designing robust language-based agents.
  arXiv  Detail & Related papers  (2025-09-23T14:48:58Z)
• OMNISEC: LLM-Driven Provenance-based Intrusion Detection via Retrieval-Augmented Behavior Prompting [4.71781133841068]
  Provenance-based Intrusion Detection Systems (PIDSes) have been widely used for endpoint threat analysis.<n>Due to the evolution of attack techniques, rules cannot dynamically model all the characteristics of attackers.<n>Anomaly-based detection systems face a massive false positive problem because they cannot distinguish between changes in normal behavior and real attack behavior.
  arXiv  Detail & Related papers  (2025-03-05T02:08:12Z)
• Progressive Monitoring of Generative Model Training Evolution [1.3108652488669736]
  Deep generative models (DGMs) have gained popularity, but their susceptibility to biases and other inefficiencies remains an issue. We introduce a progressive analysis framework to monitor the training process of DGMs. We demonstrate how our method supports identifying and mitigating biases early in training a Generative Adversarial Network (GAN)
  arXiv  Detail & Related papers  (2024-12-17T10:20:29Z)
• Attention Tracker: Detecting Prompt Injection Attacks in LLMs [62.247841717696765]
  Large Language Models (LLMs) have revolutionized various domains but remain vulnerable to prompt injection attacks. We introduce the concept of the distraction effect, where specific attention heads shift focus from the original instruction to the injected instruction. We propose Attention Tracker, a training-free detection method that tracks attention patterns on instruction to detect prompt injection attacks.
  arXiv  Detail & Related papers  (2024-11-01T04:05:59Z)
• Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens [6.329470650220206]
  The aim of this work is to highlight the relatively under-appreciated complexity of designing a monitoring strategy. We consider an ML-based risk prediction algorithm for predicting unplanned readmissions. Results from this case study emphasize the seemingly simple (and obvious) fact that not all monitoring systems are created equal.
  arXiv  Detail & Related papers  (2023-11-20T00:15:16Z)
• Model-Based Runtime Monitoring with Interactive Imitation Learning [30.70994322652745]
  This work aims to endow a robot with the ability to monitor and detect errors during task execution. We introduce a model-based runtime monitoring algorithm that learns from deployment data to detect system anomalies and anticipate failures. Our method outperforms the baselines across system-level and unit-test metrics, with 23% and 40% higher success rates in simulation and on physical hardware.
  arXiv  Detail & Related papers  (2023-10-26T16:45:44Z)
• Towards Sequence-Level Training for Visual Tracking [60.95799261482857]
  This work introduces a sequence-level training strategy for visual tracking based on reinforcement learning. Four representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP, consistently improve by incorporating the proposed methods in training.
  arXiv  Detail & Related papers  (2022-08-11T13:15:36Z)
• Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking [58.14267480293575]
  We propose a simple yet effective online learning approach for few-shot online adaptation without requiring offline training. It allows an in-built memory retention mechanism for the model to remember the knowledge about the object seen before. We evaluate our approach based on two networks in the online learning families for tracking, i.e., multi-layer perceptrons in RT-MDNet and convolutional neural networks in DiMP.
  arXiv  Detail & Related papers  (2021-12-28T06:51:18Z)
• Learning to Detect: A Data-driven Approach for Network Intrusion Detection [17.288512506016612]
  We perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy. We demonstrate the advantage of the unsupervised representation learning model in binary intrusion detection tasks.
  arXiv  Detail & Related papers  (2021-08-18T21:19:26Z)
• Anomaly Detection in Cybersecurity: Unsupervised, Graph-Based and Supervised Learning Methods in Adversarial Environments [63.942632088208505]
  Inherent to today's operating environment is the practice of adversarial machine learning. In this work, we examine the feasibility of unsupervised learning and graph-based methods for anomaly detection. We incorporate a realistic adversarial training mechanism when training our supervised models to enable strong classification performance in adversarial environments.
  arXiv  Detail & Related papers  (2021-05-14T10:05:10Z)
• Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation [7.979659145328856]
  Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses data privacy, security, access rights and access to heterogeneous information issues. Despite its advantages, there is an increased potential for cyberattacks on FL-based ML techniques that can undermine the benefits. We propose attestedFL, a defense mechanism that monitors the training of individual nodes through state persistence in order to detect a malicious worker.
  arXiv  Detail & Related papers  (2021-01-24T20:52:55Z)
• Human-in-the-Loop Imitation Learning using Remote Teleoperation [72.2847988686463]
  We build a data collection system tailored to 6-DoF manipulation settings. We develop an algorithm to train the policy iteratively on new data collected by the system. We demonstrate that agents trained on data collected by our intervention-based system and algorithm outperform agents trained on an equivalent number of samples collected by non-interventional demonstrators.
  arXiv  Detail & Related papers  (2020-12-12T05:30:35Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.