Federated Learning Research Topics

Federated Learning research topics is now widely used in many applications. It is used to secure the data and we propose this to address several existing technology issues. Here we provide several applications, metrics and techniques to be used for this research:

  1. Define Federated Learning with Raspberry Pi Zero W using fog computing

At the first stage we learn about the definition of Federated Learning with Raspberry Pi Zero W using fog computing, to permit security maintenance, optimizing edge computing for effective data processing, collaborative model training across distributed devices and machine learning when decreasing data transfer and latency.

  1. What is Federated Learning with Raspberry Pi Zero W using fog computing?

Next to the definition we learn about the detailed explanation for Federated Learning with Raspberry Pi Zero W using fog computing, it is a localized, security-maintenance machine learning technique that permits collaborative model training over a network of edge devices. In decreases data transfer, improves data security and optimizes edge computing for effective data processing and model training.

  1. Where Federated Learning with Raspberry Pi Zero W using fog computing used?

Then after the detailed explanation we discuss were to utilize Federated Learning with Raspberry Pi Zero W using fog computing, it is incorporated with the applications like IoT, finance, Smart cities and healthcare and is also used where effective edge-based processing, collaborative model training and data security are important.

  1. Why Federated Learning with Raspberry Pi Zero W using fog computing technology proposed? , previous technology issues

We propose a Federated Learning with Raspberry Pi Zero W using fog computing and it overcomes existing technology problems on the basis of latency, data security and effective edge-based processing. It also offers localized technique to collaborative model training, enhancing data privacy, privacy-maintenance and optimizing data processing in edge computing situations.

  1. Algorithms / protocols

In this research we propose a novel technique federated learning with fog computing by overcoming several previous technology issues. Here we offer some methods/Algorithms to be used for this research are Dynamic Voltage and Frequency Scaling, Laplace Noise Mechanism, Shortest Job First with Share Scheduling and Asynchronous Federated Learning are the techniques to be used for this research.

  1. Comparative study / Analysis

In the comparative analysis section we have to compare the different methods or techniques to be utilized for this research. Some of the methods to be compared are as follows:

  • Focus on data integrity by incorporating the mеthods like duplicate entry removal, Regression imputation, Adequate Outlier Normalization and Cross-field Validations.
  • The Shortest Job First (SJF) technique is used for optimization of resource allocation, and File share scheduling to give preference to urgent data.
  • Execution of differential privacy measures like Laplace Noise Mechanism, for improved data privacy and protection.
  • Safe, Security-maintaining federated learning over Asynchronous Federated Averaging.
  • Using Dynamic Voltage and Frequency Scaling (DVFS) for Energy Consumption Optimization.
  • For enhanced data transmission we use Open Shortest Path First (OSPF) routing protocol and MQTT, make sure timely information delivery in the fog computing layer.
  1. Simulation results / Parameters

For the proposed Federated Learning with Raspberry Pi Zero W using fog computing we address several issues in the existing technologies and here we provide metrics to be utilized for this research are Energy consumption (kj) and Time (sec) with Training Accuracy (%) and the Energy Consumption (j) with Data Size (kb) and Transmission Ratio with Number of Messages and Model Accuracy with Privacy.

  1. Dataset LINKS / Important URL

Federated learning with Fog Computing is proposed in this research and here we provide few important links to be utilized for gothrough the details to be required for the understanding of FL with Raspberry Pi Zero W using fog computing

  1. Federated Learning with Raspberry Pi Zero W using fog computing Applications

Now we offer the applications for Federated Learning with Raspberry Pi Zero W using fog computing is different and contains:

  • Industrial IoT: For predictive support and quality control we use collaborative machine learning technique.
  • IoT: Effective examination of sensor data when protecting security.
  • Healthcare: To improve patient data secure and combined medical investigation.
  • Smart Cities: Enhancing urban services and framework with the data-driven understandings.
  • Finance: In finance it is used for fraud detection and safe financial data exploration.
  1. Topology for Federated Learning with Raspberry Pi Zero W using fog computing

The topology to be employed for Federated Learning with Raspberry Pi Zero W using fog computing usually includes a deconcentration, connection among one system to another network structure where the with Raspberry Pi Zero W devices at the edge communication over fog computing layer and the fog computing layer handles security maintenance, data aggregation and collaborative model training. This topology will improve data security, decreases latency and optimizes data processing.

  1. Environment in Federated Learning with Raspberry Pi Zero W using fog computing

Here we discuss the environment to be used for Federated Learning with Raspberry Pi Zero W using fog computing, it is described by the distributed edge devices namely Raspberry pi Zero W single-board computers, which process and gathers data locally and these devices are generally utilized in different locations like financial institutions, IoT networks, healthcare facilities, industrial settings and Smart cities. While the fog computing framework handles the network and enables collaborative training when maintaining data security and data processing at the edge.

  1. Simulation tools

The proposed Federated Learning with Raspberry Pi Zero W using fog computing addresses several existing technology problems and is now used in various applications. The software requirements to be needed for this research are as follows: the tool used for execution is Matlab-R2020a or and above with Raspberry Pi Board module and is operated by using the OS namely Windows 10 – (64 bit).

  1. Results

Federated Learning with Raspberry Pi Zero W using fog computing is proposed in this research and in this we overcome several existing technology issues. This system is widely utilized in many applications and here we compared different metrics to obtain best findings. This can be executed by utilizing the tool Matlab-R2020a or and above with Raspberry Pi Board module.

Federated Learning Research Ideas:

The following are the research topics related to FL with fog computing that are useful when we go through the descriptions or the explanations or any other related queries about this research:

  1. F2MKD: Fog-enabled Federated Learning with Mutual Knowledge Distillation
  2. Malicious Models-based Federated Learning in Fog Computing Networks
  3. On Demand Fog Federations for Horizontal Federated Learning in IoV
  4. Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing
  5. Distributed Fog Computing and Federated-Learning-Enabled Secure Aggregation for IoT Devices
  6. Offloading Using Traditional Optimization and Machine Learning in Federated Cloud–Edge–Fog Systems: A Survey
  7. A Federated Learning Framework for Resource Constrained Fog Networks
  8. PPIoV: A Privacy Preserving-Based Framework for IoV- Fog Environment Using Federated Learning and Blockchain
  9. Timely Anomalous Behavior Detection in Fog-IoT Systems using Unsupervised Federated Learning
  10. A Low-Latency Fog-based Framework to secure IoT Applications using Collaborative Federated Learning
  11. FedFog: Network-Aware Optimization of Federated Learning Over Wireless Fog-Cloud Systems
  12. Multi-Stage Hybrid Federated Learning Over Large-Scale D2D-Enabled Fog Networks
  13. Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission
  14. Federated Learning and Blockchain-Enabled Fog-IoT Platform for Wearables in Predictive Healthcare
  15. Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach
  16. Privacy-Preserving and Poisoning-Defending Federated Learning in Fog Computing
  17. Federated Learning-Based Content Popularity Prediction in Fog Radio Access Networks
  18. Coordinated Scheduling and Decentralized Federated Learning Using Conflict Clustering Graphs in Fog-Assisted IoD Networks
  19. Multi-view Ensemble Federated Learning for Efficient Prediction of Consumer Electronics Applications in Fog Networks
  20. FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments
  21. Multi-objectives reinforcement federated learning blockchain enabled Internet of things and Fog-Cloud infrastructure for transport data
  22. Federated learning based QoS-aware caching decisions in fog-enabled internet of things networks
  23. Fed-ESD: Federated learning for efficient epileptic seizure detection in the fog-assisted internet of medical things
  24. Privacy-preserved learning from non-iid data in fog-assisted IoT: A federated learning approach
  25. Reliable federated learning in a cloud-fog-IoT environment
  26. Fog Computing Federated Learning System Framework for Smart Healthcare
  27. RSITS: Road Safety Intelligent Transport System in Deep Federated Learning Assisted Fog Cloud Networks
  28. Federated learning empowered mobility-aware proactive content offloading framework for fog radio access networks
  29. Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications
  30. Adaptive Federated Learning-based Joint Pilot Design and Active User Detection in Scalable Cell-free Massive MIMO Systems
  31. FedLC: Optimizing Federated Learning in Non-IID Data via Label-Wise Clustering
  32. An asynchronous federated learning focusing on updated models for decentralized systems with a practical framework
  33. Wireless Federated Learning With Asynchronous and Quantized Updates
  34. Decentralized Federated Learning Via Mutual Knowledge Distillation
  35. TinyFL: On-Device Training, Communication and Aggregation on a Microcontroller For Federated Learning
  36. FedCom: Byzantine-Robust Federated Learning Using Data Commitment
  37. Resource-Efficient Federated Clustering with Past Negatives Pool
  38. Shuffled Differentially Private Federated Learning for Time Series Data Analytics
  39. Joint Communication and Learning Design of Differential Privacy for Federated Learning over Multi-cell Networks
  40. Detecting Malicious Blockchain Attacks through Flower using Horizontal Federated Learning: An Investigation of Federated Approaches
  41. Analysis of Resource Usage Management Plan for Federated Learning in Hybrid Cloud
  42. Layer-wise Knowledge Distillation for Cross-Device Federated Learning
  43. FedLoop: A P2P Personalized Federated Learning Method on Heterogeneous Data
  44. Federated Learning Scheme Based on Gradient Compression and Local Differential Privacy
  45. A Blockchain-Based Federated Learning for Smart Homes
  46. Energy Aware Federated Learning with Application of Activity Recognition
  47. Energy Minimization for Wireless-Powered Federated Learning Network With NOMA
  48. Advancing Decentralized IoT with Privacy-preserving AI: Harnessing Federated Learning and NLP Techniques
  49. ECSM: An Ensembled Client Selection Mechanism for Efficient Federated Learning
  50. Differential Privacy Federated Learning based Privacy-Preserving Transmission Scheme of Distributed PV Stations