Tabla de Contenidos: “…6.3 Strong assumptions made by SOTA attacks -- 6.3.1 The state-of-the-art attacks -- 6.3.2 Strong assumptions -- Assumption 1: Knowledge of BatchNorm statistics -- Assumption 2: Knowing or being able to infer private labels -- 6.3.3 Re-evaluation under relaxed assumptions -- Relaxation 1: Not knowing BatchNorm statistics -- Relaxation 2: Not knowing private labels -- 6.4 Defenses against the gradient inversion attack -- 6.4.1 Encrypt gradients -- 6.4.2 Perturbing gradients -- 6.4.3 Weak encryption of inputs (encoding inputs) -- 6.5 Evaluation -- 6.5.1 Experimental setup -- 6.5.2 Performance of defense methods -- 6.5.3 Performance of combined defenses -- 6.5.4 Time estimate for end-to-end recovery of a single image -- 6.6 Conclusion -- 6.7 Future directions -- 6.7.1 Gradient inversion attacks for text data -- 6.7.2 Gradient inversion attacks in variants of federated learning -- 6.7.3 Defenses with provable guarantee -- References -- 2 Emerging topics -- 7 Personalized federated learning: theory and open problems -- 7.1 Introduction -- 7.2 Problem formulation of pFL -- 7.3 Review of personalized FL approaches -- 7.3.1 Mixing models -- 7.3.2 Model-based approaches: meta-learning -- 7.3.3 Multi-task learning -- 7.3.4 Weight sharing -- 7.3.5 Clients clustering -- 7.4 Personalized FL algorithms -- 7.4.1 pFedMe -- 7.4.2 FedU -- 7.5 Experiments -- 7.5.1 Experimental settings -- 7.5.2 Comparison -- 7.6 Open problems -- 7.6.1 Transfer learning -- 7.6.2 Knowledge distillation -- 7.7 Conclusion -- References -- 8 Fairness in federated learning -- 8.1 Introduction -- 8.2 Notions of fairness -- 8.2.1 Equitable fairness -- 8.2.2 Collaborative fairness -- 8.2.3 Algorithmic fairness -- 8.3 Algorithms to achieve fairness in FL -- 8.3.1 Algorithms to achieve equitable fairness -- 8.3.2 Algorithms to achieve collaborative fairness…”