FedPer (Federated Personalization) is a personalized federated learning approach that splits the model into shared base layers and private personalization layers — the base layers are federated (shared across clients), while the top layers remain local to each client for personalized predictions.

How FedPer Works

• Base Layers: Lower/feature extraction layers are shared and aggregated globally via FedAvg.
• Personalization Layers: Top layers (typically the classifier head) stay local — not shared.
• Training: Each client trains the full model, sends only base layer updates, and keeps personalization layers private.
• Split Point: Choose which layers to share vs. keep private based on the task and heterogeneity.

Why It Matters

• Personalization: Each client has a personalized model that fits their local data distribution.
• Shared Features: Base layers learn general features from all clients' data — more robust feature extraction.
• Privacy: Personalization layers are never communicated — additional privacy for local patterns.

FedPer is shared foundation, personal touch — federating common feature learning while keeping task-specific decisions private and personalized.