Master’s in Federated Learning & Privacy-Preserving ML
Online master's programme of 1,900 hours in federated learning and privacy-preserving ML: FL, differential privacy, encryption and multi-party governance. 12 modules covering federated algorithms, differential privacy, cryptography for ML, trusted execution environments, attacks and defences, anonymisation, federated systems engineering and the legal framework, with Regulation (EU) 2016/679 (GDPR), ISO/IEC 27701 and ISO/IEC 20889, and an assigned technical tutor.
Master’s in Federated Learning & Privacy-Preserving ML — studying from South Africa
South Africa has a growing digital economy, with strong demand for data scientists, analysts and simulation specialists in finance, mining, telecoms and government. Major banks, mining houses and telecommunications operators such as MTN and Vodacom rely on advanced analytics for planning and operations. Universities including the University of Cape Town and Wits contribute research in machine learning and computational modelling. Protection of personal data is governed by national legislation, which shapes how analytics work is carried out.
Fees for South Africa: R 166 000 (South African rand), with instalments and employer sponsorship available.
Relevant bodies in South Africa
Typical roles
Questions from South Africa
Is a SEIUM qualification recognised by any South African professional body for data and AI?
South Africa has no single statutory register for data scientists or AI practitioners, and bodies such as the Computer Society of South Africa and the South African Qualifications Authority set their own standards. SEIUM is a private online institute whose qualifications are not state degrees and are not accredited by a national body. Recognition is decided by employers or the relevant body, so ask them directly before enrolling.
Do South African employers in data and AI value private online qualifications?
Demand for data and analytics skills in South Africa spans banking, insurance, telecoms, retail and government, and employers often assess skills through portfolios, technical interviews and experience. A private online qualification may support your CV, but it does not guarantee a job or salary outcome. Show prospective employers the syllabus and your project work to gauge their interest.
How does the workload fit with a job, and what English level do I need?
The programme is fully online and taught in English, so you need confident reading and writing skills. As it is remote, no student visa is needed to study from home. Master's programmes total 1,900 hours, postgraduate diplomas 800 hours and specialist courses 300 hours, with an assigned technical tutor and a final project. Fees can be paid in instalments, and employer sponsorship may be possible.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify privacy threats in machine learning systems.
Implement federated learning algorithms that are robust to heterogeneity.
Apply differential privacy with rigorous accounting.
Use cryptographic techniques and trusted environments for ML.
Defend federated systems against attacks.
Deploy federated platforms at scale.
Comply with the European legal framework for data protection and governance.
Who it's for
- Federated learning engineers building models across data silos.
- Privacy engineers implementing differential privacy and secure aggregation.
- Secure ML researchers studying attacks, defences and privacy auditing.
- Confidential computing architects designing enclaves and data clean rooms.
- Technical data protection consultants advising on DPIAs and privacy by design.
- Healthcare data scientists working on multi-centre clinical data.
- Engineering or science graduates looking to enter the healthcare sector.
What you'll work with
- Regulation (EU) 2016/679 (GDPR — protection of personal data)
- ISO/IEC 27701 (privacy information management)
- ISO/IEC 20889 (privacy-enhancing data de-identification techniques)
- Regulation (EU) 2022/868 (Data Governance Act)
- Regulation (EU) 2025/327 (European Health Data Space)
- Regulation (EU) 2024/1689 (AI Act)
- ISO/IEC 27559 (privacy-enhancing data de-identification framework)
- Flower
- NVIDIA FLARE
- Opacus
- OpenFHE
- TensorFlow Federated
12 modules
1.1Sensitive data and information silos1.2Privacy threats in ML1.3Membership inference attacks1.4Model inversion and extraction1.5Centralised versus federated learning1.6Cross-device and cross-silo1.7Horizontal, vertical and transfer federated learning1.8Overview of privacy-enhancing technologies (PETs)1.9Use cases in healthcare, banking and industry1.10Case study: risk analysis of a clinical model
2.1FedAvg and FedSGD2.2Non-IID data heterogeneity2.3FedProx and SCAFFOLD2.4Adaptive federated optimisation2.5Communication and compression2.6Client selection2.7Federated personalisation2.8Vertical federated learning and entity alignment2.9Convergence and theoretical analysis2.10Lab: FedAvg with Flower
3.1Epsilon-delta definition3.2Laplace and Gaussian mechanisms3.3Sensitivity and composition3.4Local differential privacy3.5DP-SGD and gradient clipping3.6Privacy accounting with RDP3.7Differential privacy in federated learning3.8Privacy-utility trade-off3.9Choosing parameters3.10Practical exercise: training with Opacus
4.1Secure aggregation4.2Partially and fully homomorphic encryption4.3CKKS and BFV schemes4.4Secure multi-party computation4.5Secret sharing4.6Zero-knowledge proofs4.7Computational cost4.8OpenFHE and TenSEAL libraries4.9Encrypted inference4.10Lab: inference with homomorphic encryption
5.1Intel SGX and TDX enclaves5.2AMD SEV and confidential computing5.3Remote attestation5.4Confidential GPUs5.5Side-channel attacks5.6Data clean room architectures5.7Comparison with cryptographic solutions5.8Cloud deployment5.9Use cases in the financial sector5.10Practical exercise: designing a data clean room
6.1Data and model poisoning6.2Backdoor attacks in federated learning6.3Robust aggregation: Krum and median6.4Gradient reconstruction attacks6.5Attribute inference6.6Malicious and Byzantine clients6.7Empirical privacy auditing6.8Machine unlearning6.9Red teaming federated systems6.10Lab: gradient reconstruction attack and defence
7.1Pseudonymisation and anonymisation7.2k-anonymity, l-diversity and t-closeness7.3Re-identification risk7.4Private synthetic data7.5Evaluating utility and privacy7.6EDPB guidance on anonymisation7.7Location and mobility data7.8Image and voice data7.9Publishing statistics7.10Case study: synthetic dataset of clinical records
8.1Flower, FATE and NVFlare frameworks8.2Orchestration and communication8.3Deployment on mobile and edge devices8.4Fault tolerance and disconnections8.5Versioning federated models8.6Monitoring without data access8.7Federated evaluation8.8Scaling to thousands of clients8.9Cost and energy8.10Practical exercise: federated deployment with NVFlare
9.1GDPR and privacy by design9.2Data protection impact assessments (DPIAs)9.3International transfers9.4European Health Data Space9.5Data Governance Act and data altruism9.6AI Act and data governance9.7Federated consortium agreements9.8ISO/IEC 277019.9Responsibilities between parties9.10Workshop: DPIA for a federated project
10.1Multi-centre medical imaging10.2Cross-institution fraud detection10.3Predictive keyboards on mobile phones10.4Predictive maintenance across manufacturers10.5Connected vehicles10.6Power grids and smart meters10.7Telecommunications10.8Pharmaceutical research10.9Lessons from real projects10.10Case study: federated hospital consortium
11.1Federated fine-tuning of LLMs11.2Federated LoRA11.3Privacy in generative models11.4Memorisation and data extraction11.5Federated learning for computer vision11.6Federated distillation11.7Client-side resources11.8Leakage risk assessment11.9Research trends11.10Lab: federated fine-tuning of a language model
12.1Scenario and parties involved12.2Threat model12.3Proposed federated architecture12.4Privacy mechanisms12.5Implementation and experiments12.6Utility and privacy evaluation12.7Legal and governance analysis12.8Deployment plan12.9Written thesis report12.10Master's Thesis: defence before a panel
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 each intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Flower, NVIDIA FLARE, Opacus, OpenFHE and TensorFlow Federated, in free, academic or evaluation versions. Testing is studied through real procedures: federated simulations with non-IID data, empirical privacy audits and multi-centre pilots. Assessment is continuous for each module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2016/679 (GDPR), ISO/IEC 27701, ISO/IEC 20889, Regulation (EU) 2022/868 (Data Governance Act), Regulation (EU) 2025/327 (European Health Data Space), Regulation (EU) 2024/1689 (AI Act), ISO/IEC 27559.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- 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.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Flower, NVIDIA FLARE, Opacus, OpenFHE and TensorFlow Federated, 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 Master's Thesis. 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 in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the 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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