Federated Learning (for Raymond Rhine)

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Part 1: Optimization fundamentals for secure federated learning
   Chapter 1: Gradient descent-type methods
      Abstract
      Acknowledgements
      1.1. Introduction
      1.2. Basic components of GD-type methods
      1.3. Stochastic gradient descent methods
      1.4. Concluding remarks
      References
   Chapter 2: Considerations on the theory of training models with differential privacy
      Abstract
      2.1. Introduction
      2.2. Differential private SGD (DP-SGD)
      2.3. Differential privacy
      2.4. Gaussian differential privacy
      2.5. Future work
      References
   Chapter 3: Privacy-preserving federated learning: algorithms and guarantees
      Abstract
      3.1. Introduction
      3.2. Background and preliminaries
      3.3. DP guaranteed algorithms
      3.4. Performance of clip-enabled DP-FedAvg
      3.5. Conclusion and future work
      References
   Chapter 4: Assessing vulnerabilities and securing federated learning
      Abstract
      4.1. Introduction
      4.2. Background and vulnerability analysis
      4.3. Attacks on federated learning
      4.4. Defenses
      4.5. Takeaways and future work
      References
   Chapter 5: Adversarial robustness in federated learning
      Abstract
      5.1. Introduction
      5.2. Attack in federated learning
      5.3. Defense in federated learning
      5.4. Conclusion
      References
   Chapter 6: Evaluating gradient inversion attacks and defenses
      Abstract
      6.1. Introduction
      6.2. Gradient inversion attacks
      6.3. Strong assumptions made by SOTA attacks
      6.4. Defenses against the gradient inversion attack
      6.5. Evaluation
      6.6. Conclusion
      6.7. Future directions
      References
Part 2: Emerging topics
   Chapter 7: Personalized federated learning: theory and open problems
      Abstract
      7.1. Introduction
      7.2. Problem formulation of pFL
      7.3. Review of personalized FL approaches
      7.4. Personalized FL algorithms
      7.5. Experiments
      7.6. Open problems
      7.7. Conclusion
      References
   Chapter 8: Fairness in federated learning
      Abstract
      Acknowledgements
      8.1. Introduction
      8.2. Notions of fairness
      8.3. Algorithms to achieve fairness in FL
      8.4. Open problems and conclusion
      References
   Chapter 9: Meta-federated learning
      Abstract
      9.1. Introduction
      9.2. Background
      9.3. Problem definition and threat model
      9.4. Meta-federated learning
      9.5. Experimental evaluation and discussion
      9.6. Conclusion
      References
   Chapter 10: Graph-aware federated learning
      Abstract
      10.1. Introduction
      10.2. Decentralized federated learning
      10.3. Multi-center federated learning
      10.4. Graph-knowledge based federated learning
      10.5. Numerical evaluation of GFL models
      10.6. Summary
      References
   Chapter 11: Vertical asynchronous federated learning: algorithms and theoretic guarantees
      Abstract
      Acknowledgements
      11.1. Introduction
      11.2. Vertical federated learning
      11.3. Convergence analysis
      11.4. Perturbed local embedding for smoothness
      11.5. Numerical tests
      References
   Chapter 12: Hyperparameter tuning for federated learning – systems and practices
      Abstract
      12.1. Introduction
      12.2. Systems resources
      12.3. Cross-device FL hyperparameters
      12.4. System challenges in FL HPO
      12.5. State-of-the-art
      12.6. Conclusion
      References
   Chapter 13: Hyper-parameter optimization in federated learning
      Abstract
      13.1. Introduction
      13.2. State-of-the-art FL-HPO approaches
      13.3. FLoRA: a single-shot FL-HPO approach
      13.4. Empirical evaluation
      13.5. Conclusion
      References
   Chapter 14: Federated sequential decision making: Bayesian optimization, reinforcement learning, and beyond
      Abstract
      Acknowledgements
      14.1. Introduction
      14.2. Federated Bayesian optimization
      14.3. Federated reinforcement learning
      14.4. Related work
      14.5. Open problems and future directions
      References
   Chapter 15: Data valuation in federated learning
      Abstract
      Acknowledgements
      15.1. Introduction
      15.2. Data valuation: motivations and incentives
      15.3. Simple valuation methods
      15.4. Related work: conventional data valuation
      15.5. Extending to the federated setting: does it work?
      15.6. Vertical data valuation: feature valuation
      15.7. Horizontal data valuation: gradient valuation
      15.8. Learning-based valuation
      15.9. Conclusion and future work
      References
Part 3: Applications & ethical considerations
   Chapter 16: Incentives in federated learning
      Abstract
      Acknowledgements
      16.1. Overview and motivation
      16.2. Problem setting
      16.3. Incentives
      16.4. Contribution evaluation
      16.5. Client selection
      16.6. Reward allocation
      16.7. Other incentives
      16.8. Monetary rewards
      16.9. Non-monetary rewards
      16.10. Conclusion and future work
      References
   Chapter 17: Introduction to quantum federated machine learning
      Abstract
      17.1. Introduction
      17.2. Quantum federated learning
      17.3. Variational quantum circuits
      17.4. Demonstration
      17.5. Advanced settings
      17.6. Discussion
      17.7. Conclusion
      References
   Chapter 18: Federated quantum natural gradient descent for quantum federated learning
      Abstract
      18.1. Introduction
      18.2. Variational quantum circuit
      18.3. Quantum natural gradient descent
      18.4. Quantum natural gradient descent for VQC
      18.5. Federated quantum natural gradient descent
      18.6. Experimental results
      18.7. Conclusion and discussion
      References
   Chapter 19: Mobile computing framework for federated learning
      Abstract
      19.1. Federated learning on mobile platforms
      19.2. Challenge in mobile-based federated learning
      19.3. Helios: a self-coordinated federated learning framework for mobile platform
      19.4. Performance evaluation
      19.5. Conclusion and future directions
      References
   Chapter 20: Federated learning for privacy-preserving speech recognition
      Abstract
      20.1. From voice protection to federated assistant
      20.2. Federated speech recognition with synthetic data
      20.3. Conclusion
      References
   Chapter 21: Ethical considerations and legal issues relating to federated learning
      Abstract
      Acknowledgements
      21.1. Introduction
      21.2. Global trends and ethical guidelines for trustworthy AI and the universal fundamental principles
      21.3. Privacy and personal data rights and the international data protection regime
      21.4. Intellectual property rights relating to federated learning systems
      21.5. Governance structure
      21.6. Conclusion
      References
Index