Secure Mobile AI

Secure Mobile AI

Building Intelligent & Responsible Apps for iOS & Android - 2 days

Mobile devices are becoming ever more capable of doing AI/ML tasks on-device: image recognition, voice, recommendation, translate, augmented reality, etc. This unlocks real benefits: faster response, offline capability, privacy. But it also brings serious challenges: model size, limited compute, energy, resource constraints, plus ethical risks (privacy leakage, bias, misuse).

This course is designed to bridge the gap between theory and practice. You will not just learn what AI can do, but how to build it into mobile apps in a secure, optimized, and responsible way. Real-world tools, case studies, hands-on labs. Over three days you’ll move from fundamentals, through model development and optimization and on to deployment and ethics, for both iOS and Android.

Learning Outcomes

By the end of the course, participants will be able to:

• Understand and compare on-device vs cloud AI, including trade-offs (latency, privacy, compute, offline) and choose appropriately for use cases.
• Use key frameworks like TensorFlow Lite (Android & iOS), Core ML, ML Kit, Apple Intelligence / equivalents, to build AI features into apps.
• Implement AI-powered features: computer vision, NLP, recommendation engines; integrate them into mobile apps with proper considerations.
• Optimize and deploy models so they run efficiently on resource constrained devices: compression, quantization, pruning, model update strategies.
• Recognize and address ethical and security risks: data privacy, bias, fairness, transparency; design apps that respect user autonomy and comply with legal & ethical norms.
• Maintain secure pipelines: secure data collection, storage, inference; ensure that AI components do not introduce vulnerabilities (e.g. injection, adversarial attacks, reverse engineering).

Prerequisites

Learners should ideally have:

• Solid experience in native mobile development (Android with Kotlin/Java, iOS with Swift/Objective-C).
• Basic understanding of ML/AI concepts (what a neural network is, what overfitting means, etc.).
• Familiarity with standard software engineering practices: version control, testing, CI/CD.
• Some understanding of data handling (APIs, privacy, data permissions) though not necessary to be expert.

Detailed Training Outline

(The outline is not split by hour or session; rather, by topic/sub-topic so instructor and participants can allocate time flexibly during each day.)

Core Topics & Sub-topics

1. Fundamental AI/ML Concepts for Mobile Developers
   1. Supervised vs unsupervised learning: classification, regression, clustering
   2. Neural networks basics: architecture, forward & backward pass, activation, loss functions
   3. Generative AI: what it is, generative models vs discriminative; recent trends applicable to mobile (e.g. diffusion, lightweight LLMs)
   4. Performance metrics: accuracy, precision/recall, F1, latency, inference time, model size, energy cost
   5. Overfitting, underfitting, generalization; dataset quality issues (distribution shift, bias)
2. On-Device vs Cloud AI: Trade-offs and Frameworks
   1. Comparisons: latency, bandwidth, offline capability, user experience, privacy, cost, model size, update path
   2. Cloud AI services & APIs (pros & cons): e.g. Google’s cloud ML offerings, AWS, Azure, etc.
   3. On-device frameworks:
      1. TensorFlow Lite (incl. TFLite model conversion, edge inference)
      2. Core ML (model conversion, integration on iOS)
      3. ML Kit (Android / iOS)
      4. Apple Intelligence / recent Apple frameworks – what’s new
      5. Google’s Gemini Nano, GenAI APIs, etc.
   4. Hybrid approaches: offload and fallback, splitting models, using cloud only for heavy tasks, etc.
3. Implementing AI-Powered Features
   1. Computer Vision
      1. Image classification, object detection, segmentation
      2. Use-cases: AR overlays, pose detection, OCR, face detection
      3. SDKs / examples: Vision frameworks (iOS), ML Kit / TensorFlow Lite for Android, open models
   2. Natural Language Processing (NLP)
      1. Tokenization, embeddings, simple transformer-based models
      2. Tasks: text classification, summarization, translation, voice assistants, speech-to-text / text-to-speech
      3. Consider on-device vs cloud approach depending on resource & privacy constraints
   3. Recommendation Engines & Personalization
      1. Collaborative vs content-based vs hybrid recommendations
      2. Collecting & processing user behaviour data in privacy preserving ways
      3. Model freshness, feedback loops
   4. Generative Features (if time)
      1. Lightweight generation: small LLMs, prompt-based (on-device or via API)
      2. Use cases: autocomplete, suggestions, image style transfer
4. Mobile AI Tooling & SDKs
   1. Hands-on labs: installing and using ML Kit, use of Google’s GenAI APIs, Apple’s Core ML Tools, on-device generative capabilities
   2. Model conversion workflows: converting from frameworks like PyTorch / TensorFlow to TFLite, or to Core ML format
   3. Debugging & monitoring models on device: tools for profiling, measuring inference latency, memory, energy use
   4. Continuous model improvement: data collection, versioning, updates, possibly federated learning (at least conceptually)
5. Model Optimization, Efficiency & Deployment
   1. Model compression techniques: quantization (post-training, quantization aware), pruning, knowledge distillation
   2. Reducing inference latency: hardware acceleration (GPU, NN accelerators), use of optimized runtimes
   3. Memory footprint, battery impact and trade-offs
   4. Packaging and integrating models into app builds; managing updates (model shipped with app vs downloaded / updated separately)
   5. Security of models: protecting model IP, preventing adversarial attacks, model integrity, reverse engineering protection
6. Ethical, Privacy, and Secure AI in Mobile Context
   1. Data privacy and protection: GDPR / local-laws, permissions on iOS & Android, anonymization, minimizing data collection, consent, secure data storage and transmission
   2. Bias & fairness: identifying bias in data and models; testing and mitigation; ensuring models generalize across demographics, devices, edge cases
   3. Transparency and explainability: what users should know, how to provide explanations of model behaviour; logging and audit trails
   4. Security risks: adversarial examples; leaking sensitive info via models; API misuse; securing communication; securing on-device models against tampering
   5. Legal & regulatory frameworks: privacy laws, emerging AI regulation; ethical guidelines; standards and certifications