Han Liang

Han Liang, Ziwei Zhan, Weijie Liu et al.

Federated Learning (FL) is a distributed machine learning paradigm that achieves a globally robust model through decentralized computation and periodic model synthesis, primarily focusing on the global model's accuracy over aggregated datasets of all participating clients. Personalized Federated Learning (PFL) instead tailors exclusive models for each client, aiming to enhance the accuracy of clients' individual models on specific local data distributions. Despite of their wide adoption, existing FL and PFL works have yet to comprehensively address the class-imbalance issue, one of the most critical challenges within the realm of data heterogeneity in PFL and FL research. In this paper, we propose FedReMa, an efficient PFL algorithm that can tackle class-imbalance by 1) utilizing an adaptive inter-client co-learning approach to identify and harness different clients' expertise on different data classes throughout various phases of the training process, and 2) employing distinct aggregation methods for clients' feature extractors and classifiers, with the choices informed by the different roles and implications of these model components. Specifically, driven by our experimental findings on inter-client similarity dynamics, we develop critical co-learning period (CCP), wherein we introduce a module named maximum difference segmentation (MDS) to assess and manage task relevance by analyzing the similarities between clients' logits of their classifiers. Outside the CCP, we employ an additional scheme for model aggregation that utilizes historical records of each client's most relevant peers to further enhance the personalization stability. We demonstrate the superiority of our FedReMa in extensive experiments.

Han Liang, Jiahui Zhou, Zicheng Zhou et al.

The escalating adoption of diffusion models for applications such as image generation demands efficient parallel inference techniques to manage their substantial computational cost. However, existing diffusion parallelism inference schemes often underutilize resources in heterogeneous multi-GPU environments, where varying hardware capabilities or background tasks cause workload imbalance. This paper introduces Spatio-Temporal Adaptive Diffusion Inference (STADI), a novel framework to accelerate diffusion model inference in such settings. At its core is a hybrid scheduler that orchestrates fine-grained parallelism across both temporal and spatial dimensions. Temporally, STADI introduces a novel computation-aware step allocator applied after warmup phases, using a least-common-multiple-minimizing quantization technique to reduce denoising steps on slower GPUs and execution synchronization. To further minimize GPU idle periods, STADI executes an elastic patch parallelism mechanism that allocates variably sized image patches to GPUs according to their computational capability, ensuring balanced workload distribution through a complementary spatial mechanism. Extensive experiments on both load-imbalanced and heterogeneous multi-GPU clusters validate STADI's efficacy, demonstrating improved load balancing and mitigation of performance bottlenecks. Compared to patch parallelism, a state-of-the-art diffusion inference framework, our method significantly reduces end-to-end inference latency by up to 45% and significantly improves resource utilization on heterogeneous GPUs.