Differentially private (DP) decentralized Federated Learning (FL) allows local users to collaborate without sharing their data with a central server. However, accurately quantifying the privacy budget of private FL algorithms is challenging due to the co-existence of complex algorithmic components such as decentralized communication and local updates. This paper addresses privacy accounting for two decentralized FL algorithms within the $f$-differential privacy ($f$-DP) framework. We develop two new $f$-DP-based accounting methods tailored to decentralized settings: Pairwise Network $f$-DP (PN-$f$-DP), which quantifies privacy leakage between user pairs under random-walk communication, and Secret-based $f$-Local DP (Sec-$f$-LDP), which supports structured noise injection via shared secrets. By combining tools from $f$-DP theory and Markov chain concentration, our accounting framework captures privacy amplification arising from sparse communication, local iterations, and correlated noise. Experiments on synthetic and real datasets demonstrate that our methods yield consistently tighter $(ε,δ)$ bounds and improved utility compared to Rényi DP-based approaches, illustrating the benefits of $f$-DP in decentralized privacy accounting.

Knowledge graph alignment is the task of matching equivalent entities (that is, instances and classes) and relations across two knowledge graphs. Most existing methods focus on pure entity-level alignment, computing the similarity of entities in some embedding space. They lack interpretable reasoning and need training data to work. In this paper, we propose FLORA, a simple yet effective method that (1) is unsupervised, i.e., does not require training data, (2) provides a holistic alignment for entities and relations iteratively, (3) is based on fuzzy logic and thus delivers interpretable results, (4) provably converges, (5) allows dangling entities, i.e., entities without a counterpart in the other KG, and (6) achieves state-of-the-art results on major benchmarks.

Large Language Model (LLM) conditioning refers to instructing an LLM to generate content in accordance with the norms and values of a specific culture, beliefs of a particular political orientation, or any desired text-specified semantic conditioning. Unfortunately, prompt engineering does not ensure that LLMs behave in accordance with a desired conditioning due to the inductive bias of the pre-training and alignment datasets. Prior works have focused on fine-tuning LLMs by directly conditioning the LoRA weights; however, such methods introduce a large number of parameters. As a remedy, we propose Zhyper, a parameter-efficient factorized hypernetwork framework that generates context-aware LoRA adapters from textual descriptions. Experiments on multiple benchmarks show that Zhyper achieves competitive performance with up to 26x fewer parameters than the state-of-the-art baselines. Furthermore, we extend Zhyper to cultural alignment, demonstrating improved generalization to out-of-domain settings and a better capturing of fine-grained contextual values. Large Language Models (LLMs) have transformed Natural Language Processing (NLP), Computer Vision (CV), and machine learning (ML) more broadly. They achieve state-of-the-art performance in text generation and comprehension across diverse domains, including code synthesis (Rozi ` ere et al., 2023), mathematical reasoning (Ahn et al., 2024), scientific writing (Geng et al., 2025; Eger et al., 2025), multimodal tasks such as text-image understanding and generation (Alayrac et al., 2022), and evaluation of machine translation and related tasks (Gu et al., 2025). This success stems from scaling to millions and billions of parameters. However, this scaling requires large computational resources, motivating the search for parameter-efficient fine-tuning (PEFT) techniques. Recent advances have made it possible to adapt LLMs to task-specific criteria, which is crucial for a broader applicability and acceptance of NLP systems. A recent stream of research leverages PEFT techniques (Ding et al., 2023; Weyssow et al., 2023; Prottasha et al., 2024), e.g., Low-Rank Adaptions (LoRA) (Hu et al., 2021) to adapt for desired task-specific values in an LLM. LoRA achieves this by freezing most of the pre-trained model's parameters and introducing trainable low-rank matrices, yielding weight correction terms. However, stand-alone LoRA approaches are primarily tailored for a single-task adaptation and may lose their effectiveness in a setting where an LLM needs to be adapted to various downstream settings.

How much of Europe's oil still comes from Russia? Russia launched dozens of drones and decoy drones across Ukrainian territory, including one that hit a school building in Kyiv. Marco Rubio says implementing Gaza peace deal is'top priority' for Trump Body of'breadwinner' Thai captive held in Gaza returned home Displaced Palestinians forced to live in Gaza's graveyards

Ukraine's president has urged the European Union to back a plan to release billions of euros in frozen Russian cash to help fund the country's defence. As EU leaders met in Brussels, Volodymyr Zelensky said he hoped they would make a positive decision about using €140bn (£122bn) in Russian assets currently held in a Belgian clearing house. The controversial move would would be on top of sanctions the block has imposed on Russia - the latest on Thursday targeting the Kremlin's oil revenues. They followed US measures against Russia's oil industry earlier - the first time President Donald Trump has sanctioned Moscow as he grows frustrated over President Vladimir Putin's refusal to end the war. On Wednesday evening, the US president confirmed that a planned meeting with Putin in Budapest had been shelved indefinitely.

Understanding how life is adapting to urban environments represents an important challenge in evolutionary biology. In this work, we investigate a widely cited example of urban adaptation, Culex pi...

Found Energy, a startup in Boston, aims to harness the energy in scraps of aluminum metal to power industrial processes without fossil fuels. Since 2022, the company has worked to develop ways to rapidly release energy from aluminum on a small scale. Now it's just switched on a much larger version of its aluminum-powered engine, which it claims is the largest aluminum-water reactor ever built. Early next year, it will be installed to supply heat and hydrogen to a tool manufacturing facility in the southeastern US, using the aluminum waste produced by the plant itself as fuel. If everything works as planned, this technology, which uses a catalyst to unlock the energy stored within aluminum metal, could transform a growing share of aluminum scrap into a zero-carbon fuel. Rondo Energy just turned on what it says is the world's largest thermal battery, an energy storage system that can take in electricity and provide a consistent source of heat.

How much of Europe's oil still comes from Russia? A Russian drone has killed two Ukrainian journalists and wounded another in the eastern Ukrainian city of Kramatorsk, according to their outlet and the regional governor of the Donetsk region. Freedom Media, a state-funded news organisation, said on Thursday that Olena Gramova, 43, and Yevgen Karmazin, 33, had been killed by a Russian Lancet drone while in their car at a petrol station in the industrial city. Another reporter, Alexander Kolychev, was hospitalised after the attack. Freedom Media said that Gramova, a native of Yenakiieve in the Donetsk region, had originally trained as a "finance specialist", but turned to journalism in 2014, the year when Russia annexed Ukraine's Crimean peninsula, and started arming a separatist movement in Donetsk and Luhansk in the Donbas.

Tearful Kim Kardashian, 45, reveals doctors found brain aneurysm after MRI... as she blames stressful Kanye West divorce As royal insiders dish the dirt, this is what I'm told is the truth about Prince Andrew's daughters This is the exact plan I followed to supercharge my weight loss... and the surprising jab side-effect that cured me of my REAL problem: SUSAN ANDERSON Finance guru storms out of podcast with illegal migrants $420K in debt who insist they'deserve' new car and pool Dakota Johnson reveals her biggest'red flag' in men after Chris Martin split'Gaslighting' and'black out' fights: Kristen Bell and Dax Shepard's'volatile' marriage laid bare by insiders The secret calls and frantic meetings over Congressman's alleged affair with aide who set herself on fire in scandal that could upend Trump's future Pete Hegseth dealt another blow as judge shoots down effort to rebrand Pentagon with'warrior ethos' There's a taboo most men find repulsive... but if they can handle it, says JANA HOCKING, it's the biggest turn on ever The real reason behind Cracker Barrel's disastrous logo change... and it makes complete sense Astonishing new video shows Louvre robbers escaping in a mechanical delivery basket with £76m-worth of jewels - after evading CCTV that was'pointing the wrong way' Elon Musk's ex Grimes baffles fans with bizarre circular face tattoo as they insist inking looks like RINGWORM Putin ally accuses Trump of an'act of war' against Russia after US president imposed new oil sanctions French girl Lola, 12, who was'raped and murdered by Algerian woman' begged'please don't hurt me' before she was brutally killed, court hears Dave Grohl on'thin ice' with wife Jordyn Blum as insiders reveal her strict list of rules to save their marriage... and his plans for daughters to build relationship with his love child Amazon's delivery drivers will be forced to wear AI GLASSES that give them turn-by-turn directions to shave seconds off deliveries READ MORE: Amazon workers claim'kill switch' triggered massive outage In a bid to shave seconds off deliveries, Amazon will soon force its delivery drivers to wear smart glasses. The futuristic glasses use artificial intelligence ( AI) to feed drivers turn-by-turn directions leading up to customers' doorsteps. They're also fitted with cameras so drivers can scan packages and capture proof of delivery. Amazon claims the dystopian device will make deliveries'as safe and seamless as possible'. However, it seems not everyone agrees.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .