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From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Despite growing mitigation efforts to develop safety safeguards, such as supervised safety-oriented fine-tuning and leveraging safe reinforcement learning from human feedback, multiple concerns regarding the safety and ingrained biases in these models remain.
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements
The growing utilization of machine learning (ML) in decision-making processes raises questions about its benefits to society.
Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning
In addition to preserving prosumers' privacy, we show through evaluations that training prediction models using Federated Learning yields high accuracy for different energy resources while reducing the communication overhead.
Data-Aware Device Scheduling for Federated Edge Learning
As the data is the key component of the learning, we propose a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL.
