Distributed Machine Learning

16 lectures

Lecture notes and study resources for this subject.

01Lecture 01 · Splitting Models and Data

02Lecture 02 · Distributed Training Paradigms and Data Caching

03Lecture 03 · Model Caching for Decentralized Federated Learning

04Lecture 04 · Pipeline Parallelism Architectures in Distributed Machine Learning

05Lecture 05 · Distributed Messaging and Streaming Architectures in Machine Learning

06Lecture 06 · Quantization, Fine-Tuning, and Low-Rank Adaptation

07Lecture 07 · Distributed Data Processing with Apache Spark and PySpark

08Lecture 08 · Distributed Stochastic Gradient Descent

09Lecture 09 · Overlapping SGD, Quantized SGD, and Federated Learning

10Lecture 10 · Federated Averaging and SCAFFOLD

11Lecture 11 · SCAFFOLD and FedProx: Controlling Client Drift

12Lecture 12 · Vertical and Horizontal Federated Learning

13Lecture 13 · Proto-VFL Under Class Imbalance and Cross-View Training

14Lecture 14 · Prototype-Based Vertical Federated Learning with Unaligned Data

15Lecture 15 · Sample-Level Client Drift Mitigation and FedBSS in Federated Learning

16Lecture 16 · FedBSS Curriculum, Uncertainty Split, and FedCVT

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