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Postgraduate DiplomaData, AI, Simulation & Analytics Applications open

Diploma in FL Horizontal/Vertical and Orchestration

Online postgraduate diploma of 800 hours in horizontal/vertical federated learning and orchestration: 8 modules covering federated learning under IEEE 3652.1, horizontal FL, vertical FL, orchestration and platforms, security and privacy, evaluation and fairness, and applications and legal framework under the GDPR and AI Act, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Distinguish the scenarios and partitions of federated learning.

02

Implement horizontal FL algorithms that are robust to heterogeneity.

03

Develop vertical FL models with secure entity alignment.

04

Orchestrate federated platforms in real environments.

05

Protect federated systems against privacy and poisoning attacks.

06

Evaluate performance and fairness across participants.

07

Structure federated consortia in line with the legal framework.

Profile

Who it's for

  • Federated learning engineers building cross-silo and cross-device systems.
  • Machine learning engineers who need to train on data that cannot be centralised.
  • Data platform architects designing privacy-preserving infrastructures.
  • AI privacy specialists assessing attacks and defences.
  • MLOps engineers deploying and monitoring federated models.
  • Distributed AI researchers working on algorithms and benchmarks.
  • Engineering or science graduates looking to move into the healthcare sector.
Standards & tools

What you'll work with

  • IEEE 3652.1-2020 (guide for the architectural framework and application of federated machine learning)
  • Regulation (EU) 2016/679 (GDPR) (General Data Protection Regulation)
  • Regulation (EU) 2024/1689 on Artificial Intelligence (EU AI Act)
  • Regulation (EU) 2025/327 (European Health Data Space)
  • ISO/IEC 27001:2022 (information security management systems)
  • ISO/IEC 23053:2022 (framework for AI systems using machine learning)
  • ISO/IEC 27701:2019 (privacy information management)
  • Flower
  • FATE
  • NVIDIA FLARE
  • TensorFlow Federated
  • PySyft
Syllabus

8 modules

  1. 1.1Motivation and cross-device and cross-silo scenarios
  2. 1.2The federated averaging algorithm
  3. 1.3Horizontal and vertical partitions and federated transfer learning
  4. 1.4Non-IID statistical heterogeneity
  5. 1.5System heterogeneity
  6. 1.6Communication and rounds
  7. 1.7The IEEE 3652.1 framework
  8. 1.8Comparison with centralised learning
  9. 1.9Industrial use cases
  10. 1.10Workshop: federated feasibility analysis for a hospital consortium
  1. 2.1FedAvg and FedSGD
  2. 2.2FedProx against heterogeneity
  3. 2.3SCAFFOLD and drift correction
  4. 2.4FedNova and normalised aggregation
  5. 2.5Federated optimisers: FedAdam
  6. 2.6Personalisation with FedPer and Ditto
  7. 2.7Client selection
  8. 2.8Communication compression
  9. 2.9Stragglers and asynchrony
  10. 2.10Lab: FedAvg and FedProx with Flower
  1. 3.1Scenarios with feature partitions
  2. 3.2Entity alignment with PSI
  3. 3.3Vertical logistic regression
  4. 3.4SecureBoost federated trees
  5. 3.5Split neural networks
  6. 3.6Split learning and its variants
  7. 3.7Label leakage in vertical FL
  8. 3.8Inference in vertical settings
  9. 3.9Communication cost
  10. 3.10Lab: vertical SecureBoost with FATE
  1. 4.1Federated client-server architecture
  2. 4.2Hierarchical and decentralised topologies
  3. 4.3The Flower, FATE and NVFlare platforms
  4. 4.4Deployment with containers and Kubernetes
  5. 4.5Client and certificate management
  6. 4.6Round scheduling and fault tolerance
  7. 4.7Versioning of federated models
  8. 4.8Monitoring and logging
  9. 4.9Integration into MLOps
  10. 4.10Practical: NVFlare deployment across three sites
  1. 5.1Gradient inversion attacks
  2. 5.2Membership inference
  3. 5.3Data and model poisoning
  4. 5.4Backdoors in federated models
  5. 5.5Secure aggregation of updates
  6. 5.6Robust aggregation: Krum and median
  7. 5.7Differential privacy in FL
  8. 5.8Enclaves and trusted execution environments
  9. 5.9Privacy auditing
  10. 5.10Practical: poisoning attack and defence
  1. 6.1Federated evaluation without centralising data
  2. 6.2Per-client metrics
  3. 6.3Fairness among participants
  4. 6.4Incentives and contribution valuation
  5. 6.5Shapley value of clients
  6. 6.6Convergence and stability
  7. 6.7LEAF and FLamby benchmarks
  8. 6.8Reproducibility of experiments
  9. 6.9Performance reports
  10. 6.10Case study: evaluation of a federated medical imaging model
  1. 7.1FL in healthcare and medical imaging
  2. 7.2FL in banking and fraud
  3. 7.3FL in industry and maintenance
  4. 7.4FL on mobile devices
  5. 7.5Responsibilities under the GDPR
  6. 7.6Agreements between participants
  7. 7.7The European Health Data Space
  8. 7.8The AI Act and federated models
  9. 7.9Consortium governance
  10. 7.10Case study: banking consortium against fraud
  1. 8.1Definition of the consortium and use case
  2. 8.2Partition type and architecture
  3. 8.3Platform and deployment
  4. 8.4Running the federated training
  5. 8.5Security and privacy measures
  6. 8.6Evaluation of performance and fairness
  7. 8.7Governance agreement
  8. 8.8Conclusions and scaling
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Flower, FATE, NVIDIA FLARE, TensorFlow Federated and PySyft, in free, academic or evaluation versions. Practical work is based on real procedures for federated training across simulated hospital sites, deployment of federated platforms across several sites, and testing attacks and defences on federated models. Assessment is continuous by module, plus the final project.

Standards covered: IEEE 3652.1-2020, Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2024/1689 on Artificial Intelligence, Regulation (EU) 2025/327, ISO/IEC 27001:2022, ISO/IEC 23053:2022, ISO/IEC 27701:2019.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of IEEE 3652.1, the GDPR and the EU AI Act applied to real cases, not as a theoretical annex.
Industry software:Flower, FATE, NVIDIA FLARE, TensorFlow Federated and PySyft, in free, academic or evaluation versions.
Final project:a federated learning system for a real consortium, with deployment, security and privacy measures, fairness evaluation and a governance agreement, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Consortium and architecture
Define the consortium and use case, the partition type and the architecture.
Platform deployment
Deploy the federated platform across the participating sites.
Federated training
Run the federated training with security and privacy measures.
Evaluation and governance
Evaluate performance and fairness and draft the governance agreement.
Final project
Present conclusions and scaling options in a report and presentation, reviewed by your tutor.
Career opportunities

Where this takes you

Federated learning engineerMachine learning engineerData platform architectAI privacy specialistMLOps engineerDistributed AI researcher
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • An internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Flower, FATE, NVIDIA FLARE, TensorFlow Federated and PySyft, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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