484 research outputs found
Game among Interdependent Networks: The Impact of Rationality on System Robustness
Many real-world systems are composed of interdependent networks that rely on
one another. Such networks are typically designed and operated by different
entities, who aim at maximizing their own payoffs. There exists a game among
these entities when designing their own networks. In this paper, we study the
game investigating how the rational behaviors of entities impact the system
robustness. We first introduce a mathematical model to quantify the interacting
payoffs among varying entities. Then we study the Nash equilibrium of the game
and compare it with the optimal social welfare. We reveal that the cooperation
among different entities can be reached to maximize the social welfare in
continuous game only when the average degree of each network is constant.
Therefore, the huge gap between Nash equilibrium and optimal social welfare
generally exists. The rationality of entities makes the system inherently
deficient and even renders it extremely vulnerable in some cases. We analyze
our model for two concrete systems with continuous strategy space and discrete
strategy space, respectively. Furthermore, we uncover some factors (such as
weakening coupled strength of interdependent networks, designing suitable
topology dependency of the system) that help reduce the gap and the system
vulnerability
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Transformer-based large language models (LLMs) have demonstrated impressive
capabilities in a variety of natural language processing (NLP) tasks.
Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge
devices with limited computing, memory, and energy budgets. In this paper, we
propose Confidant, a multi-backend collaborative training framework for
customizing state-of-the-art LLMs on commodity mobile devices like smartphones.
Confidant partitions an LLM into several sub-models so that each fits into a
mobile device's memory. A pipeline parallel training mechanism is further
developed to ensure fast and efficient distributed training. In addition, we
propose a novel backend scheduler to allocate different attention heads to
heterogeneous compute hardware, including mobile CPU and GPUs, to maximize the
compute resource utilization on each edge device. Our preliminary experimental
results show that Confidant achieves at most 45.3% memory reduction and 8.03x
inference speedup in practical settings.Comment: 6 pages, 7 figures; Submitted to HotMobile 202
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