Applications based on Large Language Models (LLMs) contain a series of tasks to address real-world problems with boosted capability, which have dynamic demand volumes on diverse backends. Existing serving systems treat the resource demands of LLM applications as a blackbox, compromising end-to-end efficiency due to improper queuing order and backend warm up latency. We find that the resource demands of LLM applications can be modeled in a general and accurate manner with Probabilistic Demand Graph (PDGraph). We then propose Hermes, which leverages PDGraph for efficient serving of LLM applications. Confronting probabilistic demand description, Hermes applies the Gittins policy to determine the scheduling order that can minimize the average application completion time. It also uses the PDGraph model to help prewarm cold backends at proper moments. Experiments with diverse LLM applications confirm that Hermes can effectively improve the application serving efficiency, reducing the average completion time by over 70% and the P95 completion time by over 80%.

Federated Learning (FL) allows edge clients to collaborate in model training with data privacy preserved, yet it is known to suffer low training efficiency and model accuracy. Given that efficiency and accuracy are usually conflicting objectives, existing practices increasingly employ an adaptive scheme that changes the FL configurations (e.g., quantization or sparsification level) based on runtime training status, for which accurate training diagnosis—used for guiding the optimization actions—is crucial. However, while training diagnosis is a common task shared by different optimization schemes, existing works propose their diagnosis methods in an ad-hoc manner, which yield multiple limitations. First, the diagnosis metric in an optimization scheme may sometimes be less accurate than others; second, existing schemes fail to fully exploit the diagnosis result by applying it for only one optimization action; third, existing methods usually do not perceive cross-client data heterogeneity, failing to simultaneously enhance FL accuracy. To tackle those limitations, we make a systematical study on the training diagnosis methods of multiple optimization schemes, and propose metric grafting—replacing a scheme's diagnosis metric with a better one to improve the training performance. Moreover, to fully exploit the potential of training diagnosis, we build a system platform that supports flexible combinations of training diagnosis and optimization actions (i.e., single-diagnosis-multiple actions and multiple-diagnosis-multiple-actions). Evaluation on testbeds show that, with metric grafting and advanced diagnosis action combinations, we can substantially improve the efficiency and accuracy performance of FL.

Deep neural networks are increasingly trained in a distributed manner—in either clusters or with federated devices, where the participants jointly refine the global model with their gradients calculated locally. More often than not, those gradients are collected to a central server in a synchronous manner to avoid the negative impact of stale updates. However, when all the participants communicate their gradients to the server in such a uniform pace, the network on the server side—under intense contention—often becomes a performance bottleneck. To address this problem, for the cluster environment we propose theRound-Robin Synchronous Parallel(R2SP) scheme, which coordinates the participants to make updates in anevenly-gapped,round-robinmanner. This way, we can minimize the network contention with a minimum cost of the update quality; we also propose to incorporate adaptive batch sizing in R2SP to address the hardware heterogeneity among workers. Moreover, for the federated learning (FL) scenarios, we note that it is necessary yet challenging to apply the insight of R2SP to mitigate the network bottleneck in the FL server, given that there are a huge number of participants with unstable resources and inconsistent data distributions. To tackle those challenges, we further propose FL-R2SP, which extents the coordination units from individual participants to participantgroups—with the resource instability and data heterogeneity tackled within each group. We have implemented R2SP and FL-R2SP respectively with TensorFlow and PyTorch, and extensive EC2 experiments show that R2SP and FL-R2SP can respectively speed up model convergence for clustered and federated scenarios by over 20%.

Federated Learning (FL) is a distributed paradigm that supports collaborated model training while preserving data privacy, where clients periodically synchronize their local gradients once after multiple local iterations. Due to non-uniform data distribution and poor network condition, FL processes often suffer degraded training accuracy and efficiency. In this work, we analyze the microscopic parameter variation behaviors in FL, and find that an effective method to improve FL accuracy is to switch to more frequent synchronization at proper moments. In particular, such frequency-tuning moments—which can be detected from gradient characteristics—areheterogeneousacross different parameters. Motivated by such observations, we propose Parameter-Adaptive Synchronization (PAS), a FL scheme that adaptively tunes the synchronization period for each scalar parameter. The benefits of PAS are two-fold: By switching to more frequent synchronization when necessary, we can improve the FL training accuracy; by synchronizing different parameters independently, we can enable communication-computation overlapping and enhance the network utilization. We have theoretically demonstrated the convergence validity of PAS, and have further extended it with adaptive sparsification capability to jointly reduce the overall communication volume. We implemented PAS atop PyTorch, and extensive experiments show that it can substantially improve FL performance in both accuracy and communication efficiency.

Federated Learning (FL) is a distributed paradigm that supports collaborated model training while preserving data privacy, where clients periodically synchronize their local gradients once after multiple local iterations. Due to non-uniform data distribution and poor network condition, FL processes often suffer degraded training accuracy and efficiency. In this work, we analyze the microscopic parameter variation behaviors in FL, and find that an effective method to improve FL accuracy is to switch to more frequent synchronization at proper moments. Moreover, such moments can be detected from gradient characteristics, and are heterogeneous across different parameters. Motivated by such observations, we propose Parameter-Adaptive Synchronization (PAS), a FL scheme that adaptively tunes the synchronization period for each scalar parameter. The benefits of PAS are two-fold: By switching to more frequent synchronization when necessary, we can improve the FL training accuracy; by synchronizing different parameters independently, we can enable communication-computation overlapping and enhance the network utilization. We implemented PAS atop PyTorch, and extensive experiments show that it can substantially improve FL performance in both accuracy and communication efficiency.