Alibaba Cloud gave China’s AI industry a fresh shock on Tuesday after it announced a price cut of up to 97%, making RMB 1 ($0.14) to handle an input as large as two million tokens. The sudden notice comes just days after TikTok owner ByteDance promised an ultra-low-cost model, which the company claimed can process 2 million Chinese characters under RMB 1, for corporate customers. Meanwhile, Baidu’s following announcement of its plan to open two main models – the entry-level ERNIE Lite and high-performance ERNIE Speed for free on Tuesday afternoon, further signals an escalated price war has begun aims to scramble for enterprises. [TechNode reporting]
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Excerpt from www.marktechpost.com
The rapid advancement of AI has revolutionized various industries, from healthcare to finance, by enabling sophisticated data analysis and predictive modeling. However, the traditional approach to AI, which involves centralizing vast amounts of data for training models, raises significant privacy and security concerns. Federated learning has emerged as a promising field that addresses these issues by decentralizing the training process, thus enhancing privacy and security. Let’s delve into the principles of federated learning, its benefits, challenges, and future directions, drawing insights from recent research papers.
Understanding Federated Learning
Federated learning is an ML approach in which multiple devices collaboratively train a model while keeping their data localized. Instead of sending raw data to a central server, devices compute model updates locally and only share these updates. The central server aggregates these updates to improve the global model. This decentralized approach contrasts with traditional centralized training, where data from all sources is aggregated in a single location.
