Federated Learning Wireless Networks Research Topics

Federated Learning Wireless Networks Research Topics has been widely utilized in many applications. It is one of the ways to train the AI models and it has the capacity to balance data privacy with the models development. Here we provide a few details about our proposed research.

  1. Define Federated Learning

During the initial stage we first see the definition for FL. It is Machine learning technique which permits a system to be practiced along more than one localized edge appliances or servers keeping internal data specimens, by not to interchanging them. In FL rather than transfer whole data to a central server for training, then the model is dispersed to the local servers or devices and training occurs regionally on these devices. Only model updates like gradients are distributed among the central server and the local devices.

  1. What is Federated Learning?

After the definition we note down the deep understandings of Federated Learning. It is the way to train the Artificial intelligence (AI) systems without looking or affecting the data, providing a path to open details to provide new AI applications. The chat bots, recommendation tools and spam filters created artificial intelligence model for modern life and gained their data-mountains of training illustrations dragged from the web or donated by customers in exchange for free music, emails and other gains. Most of the AI applications are trained on data collection and crushed in one place. But now AI is altered toward a localized technique. Novel AI models are trained together on the edge, on data that not only leave your laptop, mobile phone or private server.

  1. Where Federated Learning used?

Succeeding the deep understanding, we now converse about where to utilize the FL. It is employed in different industries and fields where decentralization, security and collaborative model training are essential considerations. Its capacity and flexibility to handle data security through the model development create it an adaptable method over applications across an extensive range of domains and industries. Its acceptance stays to improve the association that finds to utilize gathering data interpretations while respecting data security limitations and the user selections.

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

Here the FL is proposed as an answer to overcome some of the primary problems and difficulties with the traditional centralized machine learning techniques. Several issues in the previous technologies are Data Security, Data Imbalance, Privacy Concerns and Data Transfer and Storage Costs. On the whole the FL is proposed to overcome the increasing requirement for the privacy-maintaining and localized machine learning techniques in the era where data safety, protection and regulatory acceptance are major issues. It provides a practical solution to these difficulties by allowing collaborative model training while decreasing the data vulnerability and exchange, generating a prospective technique for an extensive range of applications and industries.

  1. Algorithms / Protocols

We have to propose this research by overcoming the previous technology issues with the assistance of some novel techniques or algorithms. The method or algorithms that we utilized are as follows: Red Fox Optimization Algorithm (RFO), Hybrid Tweak Capsule Network (TCapsNet), Graph Convolutional Network (GCN), Weighted Density-based Spatial Clustering of Applications with Noise and Radio-map Reconstruction (WDBSCANR) and Model- Contrastive Federated Learning (MCFL).

  1. Comparative Study / analysis

Above we mentioned are the methods or techniques to be utilized for our proposed research. Here we compare the methods to gain the possible correct outcome.

  • The clustering is achieved by using Weighted Density-based Spatial Clustering of Applications with Noise and Radio-map Reconstruction (WDBSCANR) to decrease the latency, and the sub-static anchor nodes are employed to overcome the dynamic challenges and signal
  • To reduce the difficulty, the model contrastive FL is used to improve security and the local technique is created over the 3D constructed map by incorporating a hybrid of TCapsNet and GCN.
  • MCFL in WSN setting to enhance the Ultra-Wideband (UWB) bidirectional localization.
  • For channel evaluation RFO is utilized and that is an important factor of optimizing the achievement of wireless sensor networks.
  1. Simulation results / Parameters

Let’s see our proposed research performance by comparing the parameters or performance metrics with the existing technologies and show that our research will give the best findings. The metrics that we compared are Latency, Simulation time and Number of tag nodes with the Number of packet transmission and the correct rate, LOS accuracy and NLOS accuracy with the Number of epochs.

  1. Dataset LINKS / Important URL

In this we propose a FL based research and it overcomes the issues in the existing technologies. Then we provide the following links to verify the clarifications on the basis of this research.

  1. Federated Learning Applications

FL now has an extensive range of applications among different fields and industries. It has the capacity to train machine learning models on the localized data when maintaining the secrecy and safety it generates for many situations. We give some important FL applications such as Agriculture, Finance, Healthcare, Mobile devices, Autonomous vehicles, Edge devices and IoT, Telecommunication, manufacturing and Natural Language Processing.

  1. Topology for Federated Learning

Now we see the topology to be employed for this proposed research. It can be executed by using the different network topologies, on the basis of particular conditions, the interaction framework that is accessible and the number of servers or devices. The most common network topologies for FL are Cluster, Peer-to-Peer, Mesh, Hierarchical, client server and the Ring.

  1. Environment in Federated Learning

The environment that we used for this FL is applied in different environments, on the basis of particular conditions, presented framework and the nature of the data. Here we provide some general environments where the FL can be applied are Distributed, Private and secure, Edge computing, hybrid, cloud computing, on-premises and Cross-device.

  1. Simulation Tools

Our proposed FL based research has the following software requirements to be required. The requirements that are used by this research are as follows. The Network Simulator here we used is the NS 3.26 version. Then the work is executed by employing the programming language namely JavaScript (Node.js). The operating system used for this research is Ubuntu 14.04 LTS.

  1. Results

We propose a federated learning based technique for indoor localization in WSN technology. In this we compared several methods and then the performance metrics we used are compared with some existing technologies to validate that our proposed research gives the best finding when compared to the other existing technologies. Then the work is executed by using the operating system Ubuntu 14.04 LTS.

Federated Learning Wireless Networks Research Ideas

Subsequently we utilize the following research topics related to FL to clear the doubts or if we need any information about this go through these topics to obtain that.

  1. Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication
  2. Hensel’s Compression-Based Dimensionality Reduction Approach for Privacy Protection in Federated Learning
  3. A Federated Learning Approach for Anomaly Detection in High Performance Computing
  4. A Scalable Asynchronous Federated Learning for Privacy-Preserving Real-Time Surveillance Systems
  5. Federated Learning for Pedestrian Detection in Vehicular Networks
  6. Federated Learning for Lidar Super Resolution on Automotive Scenes
  7. Efficient Federated Learning Method for Cloud-Edge Network Communication
  8. Multimodal federated learning framework evaluation for lymph node metastasis in gynecologic malignanciese
  9. SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks
  10. Convergence-Efficient Satellite-Ground Federated Learning for LEO Mega Constellations Optical Networks
  11. Federated Learning for Water Consumption Forecasting in Smart Cities
  12. Zero-Touch MEC Resources for Connected Autonomous Vehicles Managed by Federated Learning
  13. FLB2: Layer 2 Blockchain Implementation Scheme on Federated Learning Technique
  14. Joint Receiver Design and User Scheduling for Over-the-Air Aggregation in Federated Learning
  15. Poisoning Attack based on Data Feature Selection in Federated Learning
  16. Poster: AsyncFedKD: Asynchronous Federated Learning with Knowledge Distillation
  17. Privacy-Preserving Integration of Face Recognition System and ESD Tester Using Federated Learning
  18. Medical Dataset Preparation and Privacy Preservation for Improving the Healthcare Facilities Using Federated Learning Approach
  19. A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework
  20. Long-Tailed Federated Learning Via Aggregated Meta Mapping
  21. Hwamei: A Learning-Based Synchronization Scheme for Hierarchical Federated Learning
  22. Self-adaptive and Efficient Training Node Selection for Federated Learning in B5G/6G Edge Network
  23. Outsourcing Privacy-Preserving Federated Learning on Malicious Networks through MPC
  24. Deep Learning Based Coded Over-the-Air Computation for Personalized Federated Learning
  25. Secure Federated Learning for Intelligent Industry 4.0 IoT Enabled Self Skin Care Application System
  26. Optimizing Data Distribution for Federated Learning Under Bandwidth Constraint
  27. Optimizing Federated Learning Approach: Literature Survey and Open Points
  28. Online Federated Learning based Object Detection across Autonomous Vehicles in a Virtual World
  29. Privacy-Preserving Trainer Recruitment in Model Marketplace of Federated Learning
  30. Scalable Federated Learning Simulations Using Virtual Client Engine in Flower
  31. Communication Efficient Federated Learning via Channel-wise Dynamic Pruning
  32. FedRSMax: An Effective Aggregation Technique for Federated Learning with Medical Images
  33. Incentivization and Aggregation Schemes for Federated Learning Applications
  34. Incentive-based Energy-efficient Federated Learning Aggregation for Intrusion Detection in IoT Sensor Network
  35. Autoencoder-Enhanced Federated Learning with Reduced Overhead and Lower Latency
  36. When Robotics Meets Distributed Learning: the Federated Learning Robotic Network Framework
  37. Interaction Characteristics Modeling of Microgrid Clusters Based on Federated Learning
  38. The Architecture of Computing Power Network Towards Federated Learning: Paradigms and Perspectives
  39. Federated Learning for Human Mobility
  40. Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks
  41. Abnormal Client Detection Federated Learning Using Image Vectors
  42. Blockchain-enabled Efficient and Secure Federated Learning in IoT and Edge Computing Networks
  43. FEDMBP: Multi-Branch Prototype Federated Learning on Heterogeneous Data
  44. A Fair and Efficient Federated Learning Algorithm for Autonomous Driving
  45. LAFD: Local-Differentially Private and Asynchronous Federated Learning With Direct Feedback Alignment
  46. LAFD: Local-Differentially Private and Asynchronous Federated Learning With Direct Feedback Alignment
  47. FLAGS Simulation Framework for Federated Learning Algorithms
  48. Homomorphic Encryption based Federated Learning for Financial Data Security
  49. Compressing Model before Federated Learning by Transferrable Surrogate Lottery Ticket
  50. Assessing the Implications of Data Heterogeneity on Privacy-Enhanced Federated Learning: A Comprehensive Examination Using CIFAR-10