SCAFFOLD (Stochastic Controlled Averaging for Federated Learning) is a federated learning algorithm that uses control variates to correct client drift — each client maintains a control variate that tracks the difference between local and global gradients, dramatically reducing the impact of data heterogeneity.

How SCAFFOLD Works

• Control Variates: Each client $k$ maintains $c_k$ (local control) and knows $c$ (global control).
• Corrected Update: Local SGD uses $g_k - c_k + c$ instead of raw gradient $g_k$ — subtracts local bias, adds global direction.
• Update Controls: After local training, update $c_k$ based on the local gradient drift observed.
• Communication: Send model update AND control variate update to the server.

Why It Matters

• Variance Reduction: Control variates eliminate the client drift that causes FedAvg to diverge on non-IID data.
• Fewer Rounds: SCAFFOLD converges in significantly fewer communication rounds than FedAvg on heterogeneous data.
• Theory: Provably converges at the same rate as centralized SGD, regardless of data heterogeneity.

SCAFFOLD is drift-corrected federated learning — using control variates to eliminate the client drift problem that plagues FedAvg on non-IID data.