Federated Learning on Non-IID Data Silos: An Experimental Study

🤖 Plain-English Summary

Due to the increasing privacy concerns and data regulations, training data have been increasingly fragmented, forming distributed databases of multiple “data silos” (e.g., within different organizations and countries). We find that non-IID does bring significant challenges in learning accuracy of FL algorithms, and none of the existing advanced FL algorithms outperforms others in all cases.

🔑 Key Findings

• To develop effective machine learning services, there is a must to exploit data from such distributed databases without exchanging the raw data.
• Recently, federated learning (FL) has been a solution with growing interests, which enables multiple parties to collaboratively train a machine learning model without exchanging their local data.
• A key and common challenge on distributed databases is the heterogeneity of the data distribution among the parties.

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