Federated Learning Cyber Security Research Topics

Federated Learning (FL) Cyber Security Research Topics is proposed in this research and it addresses several previous technology issues and it is widely used in many applications. We provide some details that are related to the FL based methods, techniques, applications and other information related to that.

  1. Define Federated Learning

At the beginning of every research must contain the definition. FL is otherwise known as collaborative learning and is a developing method utilized to practice a localized machine learning framework for example, deep neural network along a lot of edge devices differently from smartphones to medical wearables to vehicle to IoT appliances, etc.

  1. What is Federated Learning?

Thereafter the definition we can overview the in-depth description for FL. It is also defined as collaborative learning. It is a localized technique to prepare the Machine learning (ML) models. It will not need an interchange of information across consumer appliances to the overall hosts. Rather than using the primary information on edge appliances is utilized to prepare the framework generally, improving security of data.

  1. Where Federated Learning used?

Afterwards the in-depth description we discuss where to utilize FL. It is employed to interpret an extensive range of issues in a variety of industries, like energy, insurance, industry 4.0, logistics, telecommunication, connected vehicles and Defence. Although FL training is utilized to forecast the optimal control, diagnose malfunction, critical events, or enhance the findings in an operational process and that can be specially made on the basis of the requirements and aim of businesses.

  1. Why Federated Learning technology proposed? , Previous technology issues

In this we proposed a FL technology and it gives an answer to equalize the advantages of machine learning with the increasing difficulties on data privacy, safety and the real time difficulty of concentrating data preprocessing. It provides a localized, security-maintaining and effective way to practice ML systems when maintaining the data localized and safely. Some of the previous technology issues include Scalability, Heterogeneity and Failure Tolerance.

  1. Algorithms / Protocols

Now we see the algorithms or methods that are used for this proposed research. The methods or techniques that we used are Harmonized Google Net (HGN) algorithm, Quantum Key Secure Communication Protocol (QKSCP), Capsule Network (CapsNet) algorithm and the Dual Q Network based Asynchronous Advantage Actor Critic algorithm (DQN-A3C).

  1. Comparative study / Analysis

Here we look over the methods that we proposed to compare to get the best findings. The methods that we compared are as follows:

  • To process the gained credentials like blink patterns and fingerprint, the Capsule Network (CapsNet) method is utilized.
  • The QKCP technique is employed to reduce interaction channel exposures like Man in The Middle (MITM), reply attacks, etc.
  • In smart environments we find anomalies by incorporating DQN, a DRL algorithm and also utilized to identify and stop these kinds of attacks.
  • Verify and analyze the traffic from outside networks using the HGN XDL method.
  1. Simulation Results / Parameters

For our proposed research we have to compare various parameters or performance metrics with the previous technology to obtain the best outcome and achieve that our proposed one gives the best findings. The metrics that we compared are Prevention Accuracy, Understandability Analysis, False Positive Rate, Malicious Traffic, Detection Rate and Secure Communication Analysis.

  1. Dataset LINKS / Important URL

The FL is proposed in this research and it overcomes some existing technology issues and the following are the links that are used to go through the concepts of FL based techniques.

  1. Federated Learning Applications

Federated learning is now important in assisting secrecy-complex data applications. Here we provide some mostly used FL applications like Independent Vehicles, Healthcare, IoT, Manufacturing and industry, Mobile applications and Organization.

  1. Topology for Federated Learning

Topology in FL describes the framework or organization of the involving appliances (nodes) in a federated learning network. The topologies that were used in the FL are as follows: Mesh Topology, Hierarchical Topology, Decentralized Topology, Client server Topology and Ring Topology.

  1. Environment in Federated Learning

Let’s see the environment to be used for our proposed FL technology. It generates an efficient environment for federated learning that contains confirmed data secrecy and safety, regulatory compliance, handling scalability, choosing the right structure, setting up distributed data sources, optimizing communication, failures and addressing data distribution challenges. Continuous improvement, Monitoring, Evaluation and Documentation are also important elements for this environment.

  1. Simulation Tools

In this research we utilize the software requirements as follows and the utilized software and simulations will give the best outcome for this research. The processor utilized for this research is 2.5 GHz and above. Then the simulation tool that was used for this proposed research is NS 3.26. Now the operating system to be used to execute this work is Ubuntu 16.04 LTS (64-bit).

  1. Results

We propose a federated learning based cyber XAI research which is used to preserve and secure the data. So we propose this by overcoming several previous technology issues by comparing the metrics with the existing methods and verify that our proposed one gives the best result among all. The simulation tool that is required for implementing the work is NS 3.26.

Federated Learning Cyber Security Research Ideas:

Below we offer the topics that are relevant to the FL based system and it gives the information or descriptions about our proposed systems like methods, techniques, applications, uses and other relevant data.

  1. FedUR: Federated Learning Optimization Through Adaptive Centralized Learning Optimizers
  2. The Current State and Challenges of Fairness in Federated Learning
  3. A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
  4. Enhanced Federated Learning with Adaptive Block-wise Regularization and Knowledge Distillation
  5. Research on Power Marketing Data Sharing Framework Based on Semi-supervised Vertical Federated Learning
  6. K-FL: Kalman Filter-Based Clustering Federated Learning Method
  7. A Group Anonymity Based Federated Learning Framework
  8. Semi-Supervised Federated Learning for Keyword Spotting
  9. Study on Hyperparameter Adaptive Federated Learning
  10. FedOES: An Efficient Federated Learning Approach
  11. Federated Learning for Robust Computer Vision in Intelligent Transportation Systems
  12. Federated Learning Empowered V2V Resource Allocation in IRS-assisted Vehicular Networks
  13. Prediction of Hospital Readmission using Federated Learning
  14. FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation
  15. Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications
  16. Securing Federated Learning through Blockchain and Explainable AI for Robust Intrusion Detection in IoT Networks
  17. GAN-Based Covert Communications Against an Adversary with Uncertain Detection Threshold in Federated Learning Networks
  18. Lightweight and Data-imbalance-aware Defect Detection Approach Based on Federated Learning in Industrial Edge Networks
  19. Personalization Disentanglement for Federated Learning
  20. Split and Federated Learning with Mobility in Vehicular Edge Computing
  21. On-Demand-FL: A Dynamic and Efficient Multicriteria Federated Learning Client Deployment Scheme
  22. Federated Learning for Diabetic Retinopathy Detection in a Multi-center Fundus Screening Network
  23. Privacy-preserving Federated Learning System for Fatigue Detection
  24. Integrated Distributed Wireless Sensing with Over-The-Air Federated Learning
  25. Research on Federated Learning Data Management Method Based on Data Lake Technology
  26. Anomaly detection using Federated Learning: A Performance Based Parameter Aggregation Approach
  27. MDPFL:A Multiple Differential Privacy Protection Method based on Federated Learning
  28. Asynchronous and Synchronous Federated Learning-based UAVs
  29. Low Resource Vs High Resource solutions for Federated learning sentiment analysis
  30. FLAg: An automated client-independent federated learning system on HPC for digital pathology slice training
  31. Efficiency-Improved Federated Learning Approaches for Time of Arrival Estimation
  32. An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning
  33. Satellite Image Segmentation Using Federated Learning: A Privacy Preserving Approach
  34. A Novel Hierarchically Decentralized Federated Learning Framework in 6G Wireless Networks
  35. A Federated Learning Approach to Traffic Matrix Estimation using Super-resolution Techniques
  36. Research on Federated Learning Security Defense Technology
  37. A Deep Reinforcement Learning Approach for Federated Learning Optimization with UAV Trajectory Planning
  38. Enhancing the Efficiency of UAV Swarms Communication in 5G Networks through a Hybrid Split and Federated Learning Approach
  39. Applying Robust Gradient Difference Compression to Federated Learning
  40. Efficient Privacy-Preserving Data Aggregation for Lightweight Secure Model Training in Federated Learning
  41. A Multi-Agent System Empowered by Federated Learning and Genetic Programming
  42. IoT Intrusion Detection Based on Personalized Federated Learning
  43. Federated Learning in Malware Detection
  44. FedFingerprinting: A Federated Learning Approach to Website Fingerprinting Attacks in Tor Networks
  45. An Efficient Consensus Algorithm for Blockchain-Based Federated Learning
  46. Joint Edge Association and Aggregation Frequency for Energy-Efficient Hierarchical Federated Learning by Deep Reinforcement Learning
  47. Study of MobileNets Model in Federated Learning
  48. Pooling critical datasets with Federated Learning
  49. On the Optimization of UAV-Assisted Wireless Networks for Hierarchical Federated Learning
  50. WAFL-GAN: Wireless Ad Hoc Federated Learning for Distributed Generative Adversarial Networks