Decentralized Relaxed Smooth Optimization with Gradient Descent Methods

$L_0$-smoothness, which has been pivotal to advancing decentralized optimization theory, is often fairly restrictive for modern tasks like deep learning. The recent advent of relaxed $(L_0,L_1)$-smoothness condition enables improved convergence rates for gradient methods. Despite centralized advances, its decentralized extension remains unexplored and challenging. In this work, we propose the first general framework for decentralized gradient descent (DGD) under $(L_0,L_1)$-smoothness by introducing novel analysis techniques. For deterministic settings, our method with adaptive clipping achieves the best-known convergence rates for convex/nonconvex functions without prior knowledge of $L_0$ and $L_1$ and bounded gradient assumption. In stochastic settings, we derive complexity bounds and identify conditions for improved complexity bound in convex optimization. The empirical validation with real datasets demonstrates gradient-norm-dependent smoothness, bridging theory and practice for $(L_0,L_1)$-decentralized optimization algorithms.