splitfed learning

**SplitFed Learning** is a **hybrid approach combining Split Learning and Federated Learning** — like split learning, the model is split between clients and server, but like federated learning, multiple clients' lower-model updates are aggregated to train a shared lower model. **How SplitFed Works** - **Split**: Model is split at layer $k$ — clients have layers 1-$k$, server has layers $k+1$-$L$. - **Parallel Clients**: Multiple clients simultaneously process their data through their local lower models. - **Server Aggregation (Top)**: Server receives activations from all clients, processes through top model. - **Client Aggregation (Bottom)**: After backward pass, clients' lower models are aggregated (FedAvg style). **Why It Matters** - **Scalability**: Unlike vanilla split learning (sequential), SplitFed supports parallel client training. - **Communication**: Only intermediate activations (not full model) are communicated — reduced communication. - **Flexibility**: Combines the compute-sharing of split learning with the parallelism of federated learning. **SplitFed** is **the best of both worlds** — combining split learning's model partitioning with federated learning's parallel aggregation.