Research

Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.

Design elements that appear similar in some way — sharing the same color, shape, or size — are perceived as related, while elements that appear dissimilar are perceived as belonging to separate groups.

Although disparate views have been published, the theory underlying multimodal therapy suggests that therapist–client similarity would be most advantageous for treatment outcome and client satisfaction. To explore this question, 19 different ...

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Explore the power of pgvector and Supabase for efficient similarity search in this comprehensive guide. Keep vector data next to your business data for efficient queries and less overhead.

In multiple federated learning schemes, a random subset of clients sends in each round their model updates to the server for aggregation. Although this client selection strategy aims to reduce communication overhead, it remains energy and computationally inefficient, especially when considering resource-constrained devices as clients. This is because conventional random client selection overlooks the content of exchanged information and falls short of providing a mechanism to reduce the transmission of semantically redundant data. To overcome this challenge, we propose clustering the clients with the aid of similarity metrics, where a single client from each of the formed clusters is selected in each round to participate in the federated training. To evaluate our approach, we perform an extensive feasibility study considering the use of nine statistical metrics in the clustering process. Simulation results reveal that, when considering a scenario with high data heterogeneity of clients, similarity-based clustering can reduce the number of required rounds compared to the baseline random client selection. In addition, energy consumption can be notably reduced from 23.93% to 41.61%, for those similarity metrics with an equivalent number of clients per round as the baseline random scheme.

Federated Learning (FL) is a distributed machine learning approach in which clients contribute to learning a global model in a privacy preserved manne…

The federated learning (FL) paradigm emerges to preserve data privacy during model training by only exposing clients' model parameters rather than original data. One of the biggest challenges in FL lies in the non-IID (not identical and independently distributed) data (a.k.a., data heterogeneity) distributed on clients. To address this challenge, various personalized FL (pFL) methods are proposed such as similarity-based aggregation and model decoupling. The former one aggregates models from clients of a similar data distribution. The later one decouples a neural network (NN) model into a feature extractor and a classifier. Personalization is captured by classifiers which are obtained by local training. To advance pFL, we propose a novel pFedSim (pFL based on model similarity) algorithm in this work by combining these two kinds of methods. More specifically, we decouple a NN model into a personalized feature extractor, obtained by aggregating models from similar clients, and a classifier, which is obtained by local training and used to estimate client similarity. Compared with the state-of-the-art baselines, the advantages of pFedSim include: 1) significantly improved model accuracy; 2) low communication and computation overhead; 3) a low risk of privacy leakage; 4) no requirement for any external public information. To demonstrate the superiority of pFedSim, extensive experiments are conducted on real datasets. The results validate the superb performance of our algorithm which can significantly outperform baselines under various heterogeneous data settings.

Download scientific diagram | Understanding the impact of similarity and heterogeneity of the clients' data on their inference result at the server side for different degrees of Non-IIDness controlled by α, and number of local epochs per communication round on CIFAR-10 (Left) and on SVHN (Right). 20 clients out of 100 are randomly selected and trained for certain number of epochs (1, 5,10, and 20). The figures visualize the adjacency matrices obtained based on the inference results of some auxiliary data sampled from CIFAR-10 (Left) and that of some auxiliary data sampled from SVHN (Right) as outlined in line 3 of Algorithm 2. from publication: FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution | Conventional federated learning (FL) approaches are ineffective in scenarios where clients have significant differences in the distributions of their local data. The Non-IID data distribution in the client data causes a drift in the local model updates from the global optima,... | Inference, Federalism and Cluster Analysis | ResearchGate, the professional network for scientists.

Studies assessing the effect of similarity of client and therapist on the process or outcome of therapy have produced inconclusive results. Many have reported a...

With similarity search, we can work with semantic representations of our data and find similar items fast. And in the sections below we will discuss how exactly it works.

In this post, Couchbase breaks down what vector similarity search is, how it works, and how it can be leveraged for greater efficiency. Read on here.