Results 1 to 10 of about 7,673 (25)

Measuring Financial Advice: aligning client elicited and revealed risk [PDF]

, 2021
Financial advisors use questionnaires and discussions with clients to determine a suitable portfolio of assets that will allow clients to reach their investment objectives. Financial institutions assign risk ratings to each security they offer, and those ratings are used to guide clients and advisors to choose an investment portfolio risk that suits ...
arxiv   +1 more source

Client-to-Client Streaming Scheme for VOD Applications [PDF]

International journal of Multimedia & Its Applications 2.2 (2010) 46-55, 2010
In this paper, we propose an efficient client-to-client streaming approach to cooperatively stream the video using chaining technique with unicast communication among the clients. This approach considers two major issues of VoD 1) Prefix caching scheme to accommodate more number of videos closer to client, so that the request-service delay for the user
arxiv   +1 more source

No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices [PDF]

, 2022
Federated learning (FL) is an important paradigm for training global models from decentralized data in a privacy-preserving way. Existing FL methods usually assume the global model can be trained on any participating client. However, in real applications, the devices of clients are usually heterogeneous, and have different computing power. Although big
arxiv   +1 more source

FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning [PDF]

arXiv, 2021
Client contribution evaluation, also known as data valuation, is a crucial approach in federated learning(FL) for client selection and incentive allocation. However, due to restrictions of accessibility of raw data, only limited information such as local weights and local data size of each client is open for quantifying the client contribution.
arxiv  

FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients [PDF]

arXiv, 2022
Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing defenses mainly rely on Byzantine-robust FL methods, which aim to learn an accurate global model even if some clients are malicious.
arxiv  

Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service [PDF]

arXiv, 2023
Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select a group of appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity.
arxiv  

On the Convergence of Federated Averaging with Cyclic Client Participation [PDF]

arXiv, 2023
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL). Previous convergence analyses of FedAvg either assume full client participation or partial client participation where the clients can be uniformly sampled.
arxiv  

FilFL: Client Filtering for Optimized Client Participation in Federated Learning [PDF]

arXiv, 2023
Federated learning, an emerging machine learning paradigm, enables clients to collaboratively train a model without exchanging local data. Clients participating in the training process significantly impact the convergence rate, learning efficiency, and model generalization.
arxiv  

Client Network: An Interactive Model for Predicting New Clients [PDF]

arXiv, 2020
Understanding prospective clients becomes increasingly important as companies aim to enlarge their market bases. Traditional approaches typically treat each client in isolation, either studying its interactions or similarities with existing clients. We propose the Client Network, which considers the entire client ecosystem to predict the success of ...
arxiv  

FedSS: Federated Learning with Smart Selection of clients [PDF]

arXiv, 2022
Federated learning provides the ability to learn over heterogeneous user data in a distributed manner while preserving user privacy. However, its current client selection technique is a source of bias as it discriminates against slow clients. For starters, it selects clients that satisfy certain network and system-specific criteria, thus not selecting ...
arxiv