In　the　Industrial　Internet　of　Things　(IIoT),　model　and　computing　power　sharing　among　devices　can　improve　resource　utilization　and　work　efficiency.　However,　data　privacy　and　security　issues　hinder　the　sharing　process.　Besides,　in　the　process　of　model　sharing,　due　to　the　customization　of　industrial　equipment　functions　and　the　high　separation　of　model　and　task　types　between　devices,　it　is　difficult　to　share　model　and　optimize　models　among　devices　with　different　task　requirements.　In　this　article,　we　propose　an　adaptively　federated　multitask　learning　(AFL)　for　IIoT　devices　efficiently　model　sharing.　Inspired　by　the　parameter　sharing　mechanism,　AFL　builds　a　sparse　sharing　structure　by　designing　an　iterative　pruning　network　and　generating　subnets　for　each　task.　Moreover,　for　better　share　relevant　information,　we　further　propose　tailored　task　mask　layers　for　effectively　training　specialized　subnets,　and　an　adaptive　loss　function　to　dynamically　adjust　the　priority　between　tasks.　Extensive　experiments　show　that　AFL　can　successfully　fit　hundreds　of　tasks　from　different　devices　into　one　model,　which　preserves　both　high　accuracy　and　system　scalability,　and　outperforms　other　related　approaches　that　naively　combine　federated　learning　with　multitask　learning.