AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,117 papers
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL models often operate under the assumption of homogeneous data distributions, which frequently doesn't hold true in real-world scenarios. This researc...
Federated learning offers a compelling architecture for peacebuilding analytics in conflict-affected regions, where data sovereignty, institutional sensitivity, and infrastructural fragility preclude centralised data aggregation. We introduce a federated empirical risk minimisation objective regularised by a Wasserstei...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, a key challenge in the widespread adoption of FL is the incentive problem: motivating participants to contribute their valuable data and computational resource...
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, the inherent lack of transparency in FL raises concerns about trust, fairness, and model reliability. This paper explores the critical need for explainable AI (XAI)...
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The documentation and analysis of crimes against humanity in Uganda confront a dual computational challenge: data are distributed across sensitive, institutionally siloed repositories, and the labels required for supervised learning are exceptionally sparse due to the cost and risk of expert annotation. We introduce a...
With the development of the advanced educational platform, more and more personalized recommendation systems are used to increase the efficiency of learning and make the dissemination of the educational resources easier. However, educational recommendation tasks involve highly sensitive student data. The current centra...
Federated learning offers a promising paradigm for training machine learning models across distributed creative industry clusters without centralising sensitive cultural or commercial data. However, its deployment in Ugandan creative districts confronts two interrelated statistical challenges: distribution shift across...
Federated learning offers a promising paradigm for training machine learning models across distributed creative industry clusters without centralising sensitive cultural or commercial data. However, its deployment in Ugandan creative districts confronts two interrelated statistical challenges: distribution shift across...
The documentation and analysis of crimes against humanity in Uganda confront a dual computational challenge: data are distributed across sensitive, institutionally siloed repositories, and the labels required for supervised learning are exceptionally sparse due to the cost and risk of expert annotation. We introduce a...
This work proposes a communication-efficient PFL framework via layer-wise multi-threshold random sketching that supports bidirectional communication using compact low-bit sketches and improves the communication-accuracy tradeoff compared with existing one-bit compression approaches.
FedIoC is contributed as a modular framework in which clients fold locally available structured threat indicators into their gradient updates, and is used to pinpoint the non-IID gradient structure as the main driver of recovery and to define the open problem of designing encoders that improve on it.
Retail search systems serve diverse geographic regions with distinct query patterns, vocabularies, and product preferences, creating significant data heterogeneity that challenges both privacy-preserving training and model personalization. Federated learning offers a natural solution for privacy, but standard FL method...
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
A Blog post by IBM Research on Hugging Face
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
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