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| Considerations | Concerns | Remedies and best practices |
|
| Data privacy and compliance | Legal consequences of mishandling data | Implement strict access controls, encrypt data, data anonymization, audit trails |
| Security threats | Vulnerability to security breaches (e.g., model inversion, poisoning attacks) | Robust encryption, secure aggregation, regular threat assessments |
| Bias and fairness | Propagation of biases leading to unfair outcomes | Diverse data sources, fairness assessments, bias-awareness training |
| Informed consent | Ensuring informed consent from FL participants | Clear consent agreements., transparent communication |
| Transparency and accountability | Challenges in establishing accountability and transparency | Consortium governance, auditing mechanisms, transparency reports |
| Data quality and heterogeneity | Varied data quality and format from multiple sources | Data preprocessing, quality control |
| Education and training | Adequate training in data ethics and FL principles for project participants | Education programs, ethical guidelines |
|
