Data heterogeneity among Federated Learning (FL) users poses a significant challenge, resulting in reduced global model performance. The community has designed various techniques to tackle this issue, among which Knowledge Distillation (KD)-based techniques are common. While these techniques effectively improve performance under high heterogeneity, they inadvertently cause higher accuracy degradation under model poisoning attacks (known as attack amplification). This paper presents a case study to reveal this critical vulnerability in KD-based FL systems. We show why KD causes this issue through empirical evidence and use it as motivation to design a hybrid distillation technique. We introduce a novel algorithm, Hybrid Knowledge Distillation for Robust and Accurate FL (HYDRA-FL), which reduces the impact of attacks in attack scenarios by offloading some of the KD loss to a shallow layer via an auxiliary classifier.

If Claude Plays Pokémon is supposed to offer a glimpse of AI's future, it's not a very convincing showcase. For the past month and counting, Twitch has watched Anthropic's chatbot struggle to play Pokémon Red. Across multiple runs, Claude has failed to beat the nearly 30 year old game. And yet for David Hershey, the project's lead developer, the showcase has been a success. "I wanted some place where I could understand how Claude handles situations where it needs to work over a very long period of time," Hershey explains to me over a video call.

Federated learning (FL) allows collaborative machine learning (ML) model training among decentralized clients' information, ensuring data privacy. The decentralized nature of FL deals with non-independent and identically distributed (non-IID) data. This open problem has notable consequences, such as decreased model performance and more significant convergence times. Despite its importance, experimental studies systematically addressing all types of data heterogeneity (a.k.a. non-IIDness) remain scarce. We aim to fill this gap by assessing and quantifying the non-IID effect through a thorough empirical analysis. We use the Hellinger Distance (HD) to measure differences in distribution among clients. Our study benchmarks four state-of-the-art strategies for handling non-IID data, including label, feature, quantity, and spatiotemporal skewness, under realistic and controlled conditions. This is the first comprehensive analysis of the spatiotemporal skew effect in FL. Our findings highlight the significant impact of label and spatiotemporal skew non-IID types on FL model performance, with notable performance drops occurring at specific HD thresholds. Additionally, the FL performance is heavily affected mainly when the non-IIDness is extreme. Thus, we provide recommendations for FL research to tackle data heterogeneity effectively. Our work represents the most extensive examination of non-IIDness in FL, offering a robust foundation for future research.

SpaceX said it would review data "to better understand [the] root cause" of the most recent explosion and noted it happened after the loss of "several" engines. The Federal Aviation Administration (FAA) said the company would be required to conduct an investigation before it could fly again. Nasa hopes to use a modified version of the spaceship as a human lunar lander for its Artemis missions to return to the Moon. The tech billionaire has grand designs that the rocket system will one day take humans to the Moon, and then on to Mars, making humans "multi-planetary". He said that the first Mars mission would carry the Tesla humanoid robot "Optimus", which was shown to the public last year.

Chain-of-Thought (CoT) prompting has emerged as a pivotal technique for augmenting the inferential capabilities of language models during reasoning tasks. Despite its advancements, CoT often grapples with challenges in validating reasoning validity and ensuring informativeness. Addressing these limitations, this paper introduces the Logic Agent (LA), an agent-based framework aimed at enhancing the validity of reasoning processes in Large Language Models (LLMs) through strategic logic rule invocation. Unlike conventional approaches, LA transforms LLMs into logic agents that dynamically apply propositional logic rules, initiating the reasoning process by converting natural language inputs into structured logic forms. The logic agent leverages a comprehensive set of predefined functions to systematically navigate the reasoning process. This methodology not only promotes the structured and coherent generation of reasoning constructs but also significantly improves their interpretability and logical coherence. Through extensive experimentation, we demonstrate LA's capacity to scale effectively across various model sizes, markedly improving the precision of complex reasoning across diverse tasks.

Unlike rivals such as China and Russia, India has aligned itself with the United States by signing an agreement on space exploration, known as the Artemis Accords, a legal framework that governs activity in space. So far, nearly 30 countries have signed, allowing them to partner with the U.S. on space missions and mandating that they adhere to a set of rules, such as publicly sharing scientific discoveries and creating "safety zones" where nations could work undisturbed on the lunar surface.

In November, the San Francisco Police Department proposed approving the use of remote-controlled robots with deadly force. This was after a law came into effect requiring California officials to define the authorized use of military-grade equipment. It would have allowed police to equip robots with explosives "to contact, incapacitate or disorient violent, armed or dangerous suspects." San Francisco's Board of Supervisors approved this proposal, initially, despite opposition by civil rights groups. However, during the second of two required votes, the board voted to ban the use of lethal force by police robots. According to the San Francisco Chronicle, this is unusual as the board's second votes typically echo the first results.

The Artemis 1 Orion crew vehicle has set a new record for a NASA flight. At approximately 8:40AM ET on Saturday, Orion flew farther than any spacecraft designed to carry human astronauts had ever before, surpassing the previous record set by Apollo 13 back in 1970. As of 10:17AM ET, Orion was approximately 249,666 miles ( from 401,798 kilometers) from Earth. "Artemis I was designed to stress the systems of Orion and we settled on the distant retrograde orbit as a really good way to do that," said Jim Geffre, Orion spacecraft integration manager. "It just so happened that with that really large orbit, high altitude above the moon, we were able to pass the Apollo 13 record. But what was more important though, was pushing the boundaries of exploration and sending spacecraft farther than we had ever done before."

Back in 1969 I finally caught 2001: A Space Odyssey in a Cinerama theater in Scottsdale, Arizona. At that point, the film had been running in that theater for over a year. I had longed to see it since its release in 1968 (I remember seeing it on the marquee of a theater in downtown Indianapolis), but when we visited relatives in Phoenix the following summer the opportunity finally presented itself. After the crescendo of its end, and the credits that ran to the tune of Johann Strauss' "The Blue Danube," I stepped out of the theater in a fog, completely stunned. From the hype I had heard about the film I was expecting something of an ambitious, up-to-date Destination Moon.

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