Distributed First-Order and Second-Order Adaptive Hybrid Optimization for Multi-Robot Learning
Yilun Zhang, Xianghua Xie, Lu Zhang
AI summary
Problem
Distributed multi-robot deep learning faces a trade-off between the stability of first-order methods and the rapid convergence of second-order methods, while also needing to preserve privacy, conserve bandwidth, and manage computational overhead.
Approach
The method adaptively switches between a second-order LBFGS solver and a first-order ADMM-based solver using a trust-region convergence metric, employing a soft-switch mechanism to smooth transitions and prevent training oscillations.
Key results
- Faster convergence with fewer communication rounds than DSGD, DSGT, DiNNO, and DLBFGS
- Superior final accuracy and near-centralized performance across recognition, mapping, and reinforcement learning tasks
- Automatic, parameter-free switching eliminates manual scheduling and stabilizes training during mode transitions
- Seamless integration with standard PyTorch optimizers like Adam and LBFGS
Why it matters
Enables robust, privacy-preserving, and communication-efficient collaborative deep learning for resource-constrained multi-robot systems without relying on centralized servers.
Abstract
We present a distributed first-order and second- order adaptive hybrid optimization algorithm (DAHO) for multi-robot systems. A team of robots collaboratively trains a shared deep neural network using only local data while exchanging model updates via peer-to-peer robot commu- nication. Raw data never leaves the device, which pre- serves privacy and conserves communication bandwidth. The method blends a second-order Limited memory Broy- den–Fletcher–Goldfarb–Shann (LBFGS) method with an alter- nating direction method of multipliers (ADMM) based first- order method to obtain both the fast convergence of second- order methods and the robustness of first-order schemes. An automatic switching policy, guided by a convergence analysis rooted in trust region theory, selects the update type at each round. A soft switch mechanism derived from the same analysis mitigates oscillations during mode changes. Compared with four single-method baselines that range from first-order to second-order optimization, the proposed hybrid approach achieves faster convergence, superior accuracy, and near cen- tralized performance on robotics related deep learning tasks.