Efficient Method for ML Model Accuracy Improvement in Non-IID Data Settings

Federated learning is a machine learning approach that enables multiple parties to collaboratively train a model on their collective data without sharing the data itself. However, in real-world scenarios, the data distributed across different parties can have different distributions, leading to imbalanced data issues. This survey focuses on addressing this challenge by exploring various techniques for federated learning with non-independent and identically distributed (non-IID) data.
The article begins by discussing the challenges of federated learning with non-IID data, including the effects of data heterogeneity on model accuracy and communication efficiency. The authors highlight the importance of addressing these challenges to ensure the scalability and reliability of federated learning systems in real-world applications.
To address the challenges of non-IID data, the survey covers various techniques for federated learning, including:

1. Federated averaging: This method involves aggregating model weights from multiple parties to improve the accuracy of the model. However, this approach can be computationally expensive and communication-efficient when dealing with large datasets.
2. Weighted aggregation: This technique allows each party to assign a weight to their model weights based on their confidence in the model’s accuracy. By using these weights, the aggregator can combine the model weights from multiple parties more effectively.
3. Federated transfer learning: This method involves pre-training a model on one dataset and fine-tuning it on another non-IID dataset to improve the model’s performance. However, this approach can be computationally expensive and require large amounts of data.
4. Meta-learning: This technique enables a model to adapt to new tasks or datasets by learning how to learn from other tasks or datasets. However, this approach can be computationally expensive and may not be effective in all scenarios.
   The survey also discusses the communication cost reduction techniques for federated learning with non-IID data, including:
5. Heterogeneous data alignment: This method involves aligning the data distributions of different parties to improve the accuracy of the model. However, this approach can be computationally expensive and may not be effective in all scenarios.
6. Federated batch normalization: This technique involves normalizing the model weights from multiple parties to improve the accuracy of the model. However, this approach can be computationally expensive and may not be effective in all scenarios.
7. Gradient accumulation: This method involves accumulating gradients from multiple parties before updating the model weights to reduce communication overhead. However, this approach can be computationally expensive and may not be effective in all scenarios.
   In conclusion, the survey provides a comprehensive overview of the challenges and techniques for federated learning with non-IID data. By exploring various approaches to address these challenges, the authors demonstrate the feasibility of applying federated learning to real-world applications despite the inherent data heterogeneity. However, the survey also acknowledges the limitations of current techniques and highlights areas for future research to further improve the scalability and reliability of federated learning systems in non-IID data scenarios.