In a federated learning ecosystem, where multiple stakeholders contribute their local data to a shared model, ensuring fairness and mitigating systemic biases is crucial. To address this challenge, several strategies can be employed:
Data preprocessing and auditing: Before contributing data to the federated learning model, stakeholders should perform thorough data preprocessing and auditing to identify and address any biases present in their local data distributions.
Data normalization and standardization: Implementing data normalization and standardization techniques can help reduce the impact of local data distributions on the overall model. This can be achieved by transforming raw data into a standardized format that minimizes the effect of local data biases.
Regular model audits and bias detection: Regularly evaluating the federated learning model for biases using techniques such as fairness metrics (e.g., demographic parity, equal opportunity, and equali...
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