Outline

Tentative Outline

Part I: What is Federated Learning and Analytics

• Overview

• Characteristics

  • Cross-device vs. cross-silo FL

  • FL vs. datacenter distributed ML

  • FL vs. fully decentralized Learning

• Federated analytics

Part II: Federated Learning, Optimization, and Efficiency

• Federated Averaging (FedAvg)

  • Problem formulation

  • Challenges in FL

  • Optimization methods for FL

  • Communication-efficient FL

• FedAvg in TensorFlow Federated (TFF)

• Good baseline tasks and benchmarks

  • Best practices and newer algorithms

  • Hyperparameter tuning challenges

Part III: Privacy for Federated Learning and Analytics

• Overview

  • Actors, threat models, and privacy in depth

  • Protections against external compromised actors

  • Protections against a compromised server

• Differentially Private Federated Training

  • Central vs. local vs. distributed differential Privacy

  • Example- vs. user-level differential privacy

  • Differentially Private FedAvg

Part IV: Open Problems

• Modeling heterogeneous data

  • Personalization

  • Connections to meta-learning, multi-task, transfer learning

• Fairness and bias

• Robustness

  • Goals and capabilities of an adversary

  • Types of adversarial attacks

  • Defense mechanisms and open challenges

• System challenges

  • Cross-silo (e.g. heterogeneous data schemas)

  • cross-device (availability, reliability)