I. Introduction

II. Review Methodology
A. Objectives and Research Questions
B. Search Strategy
C. Data Extraction
D. Quality Assessment

III. 6G–IoT Intelligence: An Overview
A. Architectural Landscape
B. Supported Technological Prospects
C. 5G/6G Peer-to-Peer IoT Communications
D. 5G IoT Technologies
E. 6G IoT Technologies
F. Security and Privacy
H. Perspectives on 6G IoT

IV. An Overview of ML-Associated and 6G-Enabled IoT Security
A. Machine Learning for 6G–IoT Security
B. Machine Learning Associated Threats
C. Comparative Analysis of Edge Learning and Security in 4G, 5G, and 6G-enabled IoT Environments
D. Practical Deployments of AI in Edge/Fog

V. Edge Learning for Cyber Security
A. Differential Privacy-Based Solutions
B. Ensemble Learning-Based Solution
C. GAN-Based Solution
D. Fixed-Point Quantization Method-Based Solution
E. Deep Learning-Based Solution
F. Homomorphic Encryption-Based Solution
G. Blockchain and Smart Contracts-Based Solution
H. Dynamic Weighted Aggregation-Based Solution

VI. Datasets for Edge Learning
A. IoT-Based Multi-Purpose Security
B. Attack Classification
C. Electricity Theft Detection
D. Image Classification
E. Time Series Classification
F. Human Activity Recognition
G. Sentiment Classification
J. Application of Dataset Types in 6G IoT Security

VII. Machine Learning Vulnerabilities
A. Pre/Post-deployment Attacks
B. Backdoor Attacks
C. Adversarial Examples
D. Combined Attacks
E. Poisoning Attacks
F. Sybil Attacks
G. Byzantine Attacks
H. Inference Attacks
I. Highlights the Impacts of Vulnerabilities in Peer-to-Peer IoT Learning
J. Impact of ML Attacks on 6G Features

VIII. Defense Methods Against Edge Learning Vulnerabilities
A. Training Phase Defense Methods
B. Post-Training Phase Defense Methods
C. Inference Phase Defense Methods

IX. Lessons Learned, Open Issues, and Challenges
A. Lessons Learned
B. Open Issues and Challenges

X. Conclusions