Paper-Conference | Zhonghao Chen

FedMECA: Scalable Federated Learning via Memory-Efficient and Concurrent Aggregation

Thu, 01 Jan 2026 00:00:00 +0000

FedMECA improves federated learning scalability by making aggregation more memory-efficient and concurrent, targeting complex FL workflows with large model and client counts.

SPARe: Stacked Parallelism with Adaptive Reordering for Fault-Tolerant LLM Pretraining Systems with 100k+ GPUs

Thu, 01 Jan 2026 00:00:00 +0000

SPARe studies fault-tolerant LLM pretraining at extreme scale, combining stacked parallelism with adaptive reordering to improve resilience and efficiency for 100k+ GPU systems.

When RDMA Goes Long-Haul: Characterization, Modeling, and Verbs-Level Emulation with Implications for Federated Learning

Thu, 01 Jan 2026 00:00:00 +0000

This work characterizes long-haul RDMA behavior, develops modeling and verbs-level emulation support, and studies the implications for geo-distributed federated learning systems.

Can Long-Haul RDMA Benefit Federated Learning?

Wed, 01 Jan 2025 00:00:00 +0000

This work studies the potential of long-haul RDMA for federated learning workloads and compares RDMA and TCP/IP under geo-distributed settings.

FedDES: Discrete Event Based Performance Simulation for Federated Learning Systems

Wed, 01 Jan 2025 00:00:00 +0000

FedDES models federated learning training, communication, and aggregation as lightweight events, enabling systematic performance analysis of complex FL workflows under diverse networking conditions.

HPC-R1: Characterizing R1-like Large Reasoning Models on HPC

Wed, 01 Jan 2025 00:00:00 +0000

HPC-R1 characterizes inference and distillation performance of R1-like reasoning models on HPC platforms, identifying system bottlenecks and scalable deployment strategies.

Deep Learning Techniques for EEG-Based BCI: Analysis and Applications

Sun, 01 Oct 2023 00:00:00 +0000

Conference paper on deep learning techniques for EEG-based brain-computer interfaces (BCI).