285 research outputs found
On the Local Cache Update Rules in Streaming Federated Learning
In this study, we address the emerging field of Streaming Federated Learning
(SFL) and propose local cache update rules to manage dynamic data distributions
and limited cache capacity. Traditional federated learning relies on fixed data
sets, whereas in SFL, data is streamed, and its distribution changes over time,
leading to discrepancies between the local training dataset and long-term
distribution. To mitigate this problem, we propose three local cache update
rules - First-In-First-Out (FIFO), Static Ratio Selective Replacement (SRSR),
and Dynamic Ratio Selective Replacement (DRSR) - that update the local cache of
each client while considering the limited cache capacity. Furthermore, we
derive a convergence bound for our proposed SFL algorithm as a function of the
distribution discrepancy between the long-term data distribution and the
client's local training dataset. We then evaluate our proposed algorithm on two
datasets: a network traffic classification dataset and an image classification
dataset. Our experimental results demonstrate that our proposed local cache
update rules significantly reduce the distribution discrepancy and outperform
the baseline methods. Our study advances the field of SFL and provides
practical cache management solutions in federated learning
Regret Minimization with Noisy Observations
In a typical optimization problem, the task is to pick one of a number of
options with the lowest cost or the highest value. In practice, these
cost/value quantities often come through processes such as measurement or
machine learning, which are noisy, with quantifiable noise distributions. To
take these noise distributions into account, one approach is to assume a prior
for the values, use it to build a posterior, and then apply standard stochastic
optimization to pick a solution. However, in many practical applications, such
prior distributions may not be available. In this paper, we study such
scenarios using a regret minimization model.
In our model, the task is to pick the highest one out of values. The
values are unknown and chosen by an adversary, but can be observed through
noisy channels, where additive noises are stochastically drawn from known
distributions. The goal is to minimize the regret of our selection, defined as
the expected difference between the highest and the selected value on the
worst-case choices of values. We show that the na\"ive algorithm of picking the
highest observed value has regret arbitrarily worse than the optimum, even when
and the noises are unbiased in expectation. On the other hand, we
propose an algorithm which gives a constant-approximation to the optimal regret
for any . Our algorithm is conceptually simple, computationally efficient,
and requires only minimal knowledge of the noise distributions
Interactive Communication in Bilateral Trade
We define a model of interactive communication where two agents with private types can exchange information before a game is played. The model contains Bayesian persuasion as a special case of a one-round communication protocol. We define message complexity corresponding to the minimum number of interactive rounds necessary to achieve the best possible outcome. Our main result is that for bilateral trade, agents don\u27t stop talking until they reach an efficient outcome: Either agents achieve an efficient allocation in finitely many rounds of communication; or the optimal communication protocol has infinite number of rounds. We show an important class of bilateral trade settings where efficient allocation is achievable with a small number of rounds of communication
Monad: Towards Cost-effective Specialization for Chiplet-based Spatial Accelerators
Advanced packaging offers a new design paradigm in the post-Moore era, where
many small chiplets can be assembled into a large system. Based on
heterogeneous integration, a chiplet-based accelerator can be highly
specialized for a specific workload, demonstrating extreme efficiency and cost
reduction. To fully leverage this potential, it is critical to explore both the
architectural design space for individual chiplets and different integration
options to assemble these chiplets, which have yet to be fully exploited by
existing proposals. This paper proposes Monad, a cost-aware specialization
approach for chiplet-based spatial accelerators that explores the tradeoffs
between PPA and fabrication costs. To evaluate a specialized system, we
introduce a modeling framework considering the non-uniformity in dataflow,
pipelining, and communications when executing multiple tensor workloads on
different chiplets. We propose to combine the architecture and integration
design space by uniformly encoding the design aspects for both spaces and
exploring them with a systematic ML-based approach. The experiments demonstrate
that Monad can achieve an average of 16% and 30% EDP reduction compared with
the state-of-the-art chiplet-based accelerators, Simba and NN-Baton,
respectively.Comment: To be published in ICCAD 202
SurrogatePrompt: Bypassing the Safety Filter of Text-To-Image Models via Substitution
Advanced text-to-image models such as DALL-E 2 and Midjourney possess the
capacity to generate highly realistic images, raising significant concerns
regarding the potential proliferation of unsafe content. This includes adult,
violent, or deceptive imagery of political figures. Despite claims of rigorous
safety mechanisms implemented in these models to restrict the generation of
not-safe-for-work (NSFW) content, we successfully devise and exhibit the first
prompt attacks on Midjourney, resulting in the production of abundant
photorealistic NSFW images. We reveal the fundamental principles of such prompt
attacks and suggest strategically substituting high-risk sections within a
suspect prompt to evade closed-source safety measures. Our novel framework,
SurrogatePrompt, systematically generates attack prompts, utilizing large
language models, image-to-text, and image-to-image modules to automate attack
prompt creation at scale. Evaluation results disclose an 88% success rate in
bypassing Midjourney's proprietary safety filter with our attack prompts,
leading to the generation of counterfeit images depicting political figures in
violent scenarios. Both subjective and objective assessments validate that the
images generated from our attack prompts present considerable safety hazards.Comment: 14 pages, 11 figure
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
With the recent success of graph convolutional networks (GCNs), they have
been widely applied for recommendation, and achieved impressive performance
gains. The core of GCNs lies in its message passing mechanism to aggregate
neighborhood information. However, we observed that message passing largely
slows down the convergence of GCNs during training, especially for large-scale
recommender systems, which hinders their wide adoption. LightGCN makes an early
attempt to simplify GCNs for collaborative filtering by omitting feature
transformations and nonlinear activations. In this paper, we take one step
further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN),
which skips infinite layers of message passing for efficient recommendation.
Instead of explicit message passing, UltraGCN resorts to directly approximate
the limit of infinite-layer graph convolutions via a constraint loss.
Meanwhile, UltraGCN allows for more appropriate edge weight assignments and
flexible adjustment of the relative importances among different types of
relationships. This finally yields a simple yet effective UltraGCN model, which
is easy to implement and efficient to train. Experimental results on four
benchmark datasets show that UltraGCN not only outperforms the state-of-the-art
GCN models but also achieves more than 10x speedup over LightGCN.Comment: Paper accepted in CIKM'2021. Code available at:
https://github.com/xue-pai/UltraGC
Antidiabetic Rosiglitazone Reduces Soluble Intercellular Adhesion Molecule-1 Level in Type 2 Diabetic Patients with Coronary Artery Disease
Background. We investigated the level of soluble adhesion molecules in diabetic patients and the effect of the peroxisome proliferator-activated receptor-γ (PPAR-γ) agonist rosiglitazone on plasma levels of adhesion molecules and an inflammation marker in type 2 diabetic patients with coronary artery disease (CAD) after percutaneous coronary intervention (PCI). Methods. A total of 116 diabetic patients with CAD who had undergone PCI were randomized to receive rosiglitazone (4 mg/d) or not for 6 months. Plasma levels of soluble intercellular adhesion molecules (sICAM-1) and P-selectin (sP-selectin) were measured on ELISA. Results. After 6-month rosiglitazone treatment, plasma levels of sICAM-1 were lower than baseline and control group levels (370.4 (332.4–421.9) pg/mL versus 423.5 (327.4–500.3) pg/mL and 404.6 (345.2–483.4) pg/mL, P < .001). In addition, plasma levels of C-reactive protein were significantly reduced from baseline levels. However, plasma level of sP-selectin was not significantly lowered with rosiglitazone treatment than with control treatment after 6-month follow-up. Conclusions. Rosiglitazone reduces chronic inflammatory responses and improves levels of markers of endothelial dysfunction in patients with diabetes and CAD. PPAR-γ agonist may have a beneficial effect on the vascular endothelium through its anti-inflammatory mechanism and may be useful as therapy in patients undergoing PCI
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