Author

Zheng Xu

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Jul 2026

PowerScale: Energy-Efficient Geo-Distributed Model Training with Federated Datacenter Power

The power demands of large-scale AI training increasingly exceed the capacity of any single data center, making geo-distributed training across power-constrained sites a practical necessity. Prior work optimizes such training mainly for time-to-accuracy using single-tier aggregation, where every site exchanges model updates directly with a central aggregator over the WAN each synchronization round, without accounting for the energy required to reach convergence. Single-tier aggregation is fundamentally energy-inefficient because synchronization barriers force faster sites to idle, full WAN updates dominate communication energy at scale, and fixed synchronization frequency keeps paying the same communication cost even when updates shrink late in training. To address these inefficiencies, we present PowerScale, a hierarchical aggregation system that exploits the latency hierarchy of wide-area networks. PowerScale organizes sites into regional clusters and applies a Sync-Async synchronization modality: sites synchronize frequently with a nearby cluster aggregator over fast local links, while cluster aggregators push pre-aggregated updates asynchronously to a global aggregator over the WAN. PowerScale forms clusters based on both network proximity and power availability, and uses an adaptive synchronization policy that reduces communication energy by adjusting how often clusters synchronize to training progress. This structure shortens synchronization barriers and replaces per-site WAN transmissions with fewer, pre-aggregated transmissions at a lower frequency. We evaluate PowerScale at 100-site scale in a Flower-based simulation environment. PowerScale matches or slightly improves time-to-accuracy compared with single-tier baselines while reducing energy consumption by up to 3.9x.

Talha Mehboob, Zheng Xu, Michael Zink et al. · 0 citations

Open access Jul 2026

Decomposition-Enhanced Network for financial time series forecasting

The extreme non-stationarity, high noise levels, and multi-timescale coupling in financial futures markets pose major challenges for time series forecasting. Existing models often struggle to disentangle localized shocks from global trends due to incompatible inductive biases. To address this issue, we propose a Decomposition-Enhanced Network (DENet). Following a divide-and-conquer paradigm, DENet adopts a multi-stream architecture: the main path extracts stable trends via moving averages and dual-path linear projections, while Auxiliary Stream I captures seasonal and local cyclical patterns using depthwise separable convolutions, and Auxiliary Stream II models high-frequency dynamics through a nonlinear autoregressive-style mapping. These components are integrated via an adaptive fusion mechanism, balancing global robustness and local structural sensitivity. Experiments on real-world futures data demonstrate that DENet outperforms a wide range of state-of-the-art benchmarks. Compared with seven state-of-the-art baselines, DENet achieves an average reduction of 10.15% in RMSE for daily forecasting on iron ore futures, and an average reduction of 22.57% in MAE under the 5-min, 12-step forecasting horizon. Furthermore, we integrate DENet’s dual-scale predictions into the R-Breaker strategy with parameter switching and dynamic position sizing. Backtesting results reveal that the annualized return of iron ore futures outperforms the baseline strategy by an average of 7.6 percentage points across five backtesting windows. Ultimately, DENet effectively bridges advanced structural modeling and actionable algorithmic trading.

Jinyuan Huang, Qianqian Sun, Xinghua Zhang et al. · Scientific Reports · 0 citations