The European Union Artificial Intelligence Act (AI Act) is shaping the regulatory landscape for artificial intelligence, with significant implications for Federated Learning (FL). This paper explores how the AI Act may influence FL, emphasizing the need for FL to align with the Act's priorities, particularly in data governance, privacy, and energy efficiency. The AI Act introduces new challenges for FL, including the need for robustness, data governance, and energy efficiency, while also offering opportunities to enhance privacy and reduce data bias. FL's decentralized nature allows it to address these challenges more effectively than centralized learning, as it minimizes data movement and enables privacy-preserving computation. However, FL must adapt to meet the Act's requirements, including ensuring data privacy, compliance with GDPR, and energy efficiency. The paper highlights the need for FL research to focus on these areas to ensure compliance and adoption. It also discusses the potential for FL to become a key component of AI Act-compliant systems, particularly in high-risk applications. The AI Act's emphasis on sustainability and energy efficiency further underscores the importance of FL in reducing the environmental impact of AI. The paper calls for a shift in FL research priorities to address these challenges and opportunities, ensuring that FL can meet the regulatory and societal demands of the AI Act.The European Union Artificial Intelligence Act (AI Act) is shaping the regulatory landscape for artificial intelligence, with significant implications for Federated Learning (FL). This paper explores how the AI Act may influence FL, emphasizing the need for FL to align with the Act's priorities, particularly in data governance, privacy, and energy efficiency. The AI Act introduces new challenges for FL, including the need for robustness, data governance, and energy efficiency, while also offering opportunities to enhance privacy and reduce data bias. FL's decentralized nature allows it to address these challenges more effectively than centralized learning, as it minimizes data movement and enables privacy-preserving computation. However, FL must adapt to meet the Act's requirements, including ensuring data privacy, compliance with GDPR, and energy efficiency. The paper highlights the need for FL research to focus on these areas to ensure compliance and adoption. It also discusses the potential for FL to become a key component of AI Act-compliant systems, particularly in high-risk applications. The AI Act's emphasis on sustainability and energy efficiency further underscores the importance of FL in reducing the environmental impact of AI. The paper calls for a shift in FL research priorities to address these challenges and opportunities, ensuring that FL can meet the regulatory and societal demands of the AI Act.