Certified 6G Edge AI Engineer (6G-EAI) Certification Program by Tonex
Certified 6G Edge AI Engineer (6G-EAI) prepares you to design, deploy, and govern native AI in 6G networks. It covers distributed inference, federated learning, and closed-loop automation. You will learn placement strategies across device, edge, and core. You will balance latency, cost, and energy. The program emphasizes reliability and observability. It aligns with emerging 3GPP, O-RAN, and ETSI directions.
Cybersecurity is woven into every topic. You will mitigate model poisoning and data leakage. You will harden pipelines against adversarial traffic. You will apply zero-trust, secure aggregation, and privacy-enhancing methods. The goal is trustworthy automation that scales. Graduates can translate intents into safe network actions. They can justify trade-offs with metrics. They can lead cross-functional teams to deliver resilient 6G services.
Learning Objectives:
- Design edge AI architectures for 6G latency and throughput goals
- Orchestrate distributed inference and model placement across tiers
- Implement federated and split learning with non-IID data
- Build intent-driven, closed-loop network automation
- Apply zero-trust and privacy safeguards to edge AI
- Monitor drift, quality, and cost with clear SLOs
- Mitigate adversarial and data poisoning risks
- Align solutions with 3GPP/O-RAN/ETSI guidance
Audience:
- Network architects and planners
- Radio and core engineers
- Edge/cloud solution engineers
- Cybersecurity professionals
- AI/ML engineers and data scientists
- Product and operations leaders
Course Modules:
Module 1: 6G & Edge AI Foundations
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- 6G service-based architecture and edge tiers
- Workload characterization and latency budgets
- Model lifecycle at device, edge, and core
- Data pipelines and feature stores at the edge
- Observability, SLOs, and KPIs
- Standards landscape and roadmaps
Module 2: Distributed Inference Engineering
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- Model partitioning and tensor offload
- Caching, warm starts, and cold-start control
- Quantization and sparsity trade-offs
- Scheduling under energy and cost limits
- Adaptive routing for inference requests
- Failover and graceful degradation
Module 3: Federated & Split Learning
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- Client selection and cohorting in RAN domains
- Handling non-IID and drifted datasets
- Aggregation strategies and compression
- Secure aggregation and differential privacy
- Personalization vs. global model quality
- Compliance and data residency constraints
Module 4: Network Automation & Control
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- Intent modeling and policy translation
- Telemetry, feature extraction, and context
- Anomaly detection and auto-remediation
- RIC xApps/rApps patterns (vendor-neutral)
- Multi-domain coordination (RAN, transport, core)
- Verification, rollback, and audit trails
Module 5: Reliability, Trust, and Safety
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- Robustness to drift and rare events
- Guardrails and safe action bounding
- Explainability for operational decisions
- Human-in-the-loop escalation paths
- Cost, carbon, and performance trade-offs
- Governance and model risk management
Module 6: Security for Edge AI in 6G
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- Threats: poisoning, evasion, inversion
- Zero-trust segmentation and attestation
- Key management and secrets at the edge
- Secure boot, updates, and provenance
- Privacy-enhancing technologies and PII controls
- Compliance mapping and continuous assurance
Exam Domains:
- Communications–AI Co-Design Principles
- Edge Intelligence Orchestration & Lifecycle
- Secure Data Governance & Privacy Controls
- Real-Time Assurance, Observability & SLOs
- AI Risk, Safety, and Governance in Telecom
- Standards, Policy, and Ethical Considerations
Course Delivery:
The course uses expert-led lectures, interactive discussions, case studies, and guided exercises. Participants access curated readings and real-world examples. Activities focus on analysis and design—not simulations or labs.
Assessment and Certification:
Assessment includes quizzes, short assignments, and a capstone design brief. Upon successful completion, participants receive the Certified 6G Edge AI Engineer (6G-EAI) certificate from Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified 6G Edge AI Engineer (6G-EAI) Certification Training exam, candidates must achieve a score of 70% or higher.
Elevate your 6G edge AI skills. Build trustworthy, automated networks. Enroll with Tonex and lead the next wave of intelligent connectivity.