non-independent and identically distributed

1. • This makes it statistically impossible non-independent and to infer information about any single data point from the aggregated model.
   • While it introduces a trade-off with model accuracy, it provides strong privacy guarantees, crucial for sensitive data stored in distributed databases.

2. Addressing Data Heterogeneity (Non-IID Data)

   • One of the biggest challenges accurate cleaned numbers list from frist database in FL, especially with distributed databases, is non-IID () data.
   • Clients in a distributed environment rarely have data that follows the same distribution.
   • For example, different hospitals will have different patient demographics or disease prevalence.
   • FedProx: This algorithm modifies FedAvg by adding a proximal term to the local objective function.
   • This term penalizes local models from deviating too far from the global model, helping to stabilize training and improve how to use a phone list to optimize your fundraising campaigns convergence on non-IID data.
   • Personalized FL: Instead of aiming for a single global model, personalized FL techniques allow for training a global model that can be fine-tuned locally for each client.
   • Or even training multiple clustered models based on data similarity.

Security Protocols and Mechanisms

Beyond the algorithmic privacy measures, robust security protocols are paramount in FL with distributed databases:

• Homomorphic Encryption (HE): Enables computations on encrypted data without decryption. Clients can encrypt their model mobile phone numbers updates before sending them to the aggregator.
• The aggregator can perform the averaging non-independent and operation on the encrypted data, preserving privacy end-to-end. This is computationally intensive but offers strong guarantees.
• Secure Multi-Party Computation (SMPC): Allows multiple parties to jointly compute a function over their private inputs without revealing their inputs to each other.
• This can be used for secure aggregation of model updates, where no single party (including the aggregator) sees individual contributions.