An End-to-End Encrypted Control Pipeline for Multi-Agent Coordination via CKKS Homomorphic Encryption
For multi-agent systems requiring cloud-based coordination with privacy, this work provides a practical encrypted control pipeline, though it is incremental as it adapts existing control techniques (steady-state Kalman gains, diagonal method) to FHE constraints.
The paper presents the first end-to-end encrypted control pipeline for multi-agent coordination using CKKS homomorphic encryption, where all stages (sensing, estimation, propagation, control) operate on encrypted data. Validation on a formation control scenario shows stable closed-loop operation with bounded tracking error, and a design equation for the privacy-accuracy tradeoff is derived.
Cloud-based coordination of multi-agent systems requires sharing state with a central server, creating a conflict between coordination and privacy. Fully homomorphic encryption (FHE) resolves this in principle, but its severe arithmetic constraints demand that every stage of the control loop be redesigned from first principles. We present an end-to-end encrypted control pipeline in which sensing, state estimation, state propagation, and consensus control all operate on CKKS-encrypted data using only addition, multiplication, and cyclic rotation. In order to overcome the computational challenges of FHE, we employ steady-state Kalman gains instead of solving for the matrices online and graph Laplacians are applied via the diagonal method at a cost proportional to the number of nonzero cyclic diagonals, accommodating ring, torus, and complete-graph topologies within a unified framework. To quantify the cumulative effect of encryption noise, we use the separation principle to decouple controller and observer error dynamics and derive a periodic bootstrapping bound in which CKKS bootstrapping acts as an impulsive disturbance; the resulting steady-state error ball depends on the bootstrapping precision and the closed-loop spectral radius, providing a direct design equation for the privacy-accuracy tradeoff. The pipeline is validated on a multi-agent formation control scenario, confirming stable closed-loop operation under encryption with bounded tracking error.