Federated　Learning　(FL),　as　a　privacy-enhancing　distributed　learning　paradigm,　has　recently　attracted　much　attention　in　wireless　systems.　By　providing　communication　and　computation　services,　the　base　station　(BS)　helps　participants　collaboratively　train　a　shared　model　without　transmitting　raw　data.　Concurrently,　with　the　advent　of　integrated　sensing　and　communication　(ISAC)　and　the　growing　demand　for　sensing　services,　it　is　envisioned　that　BS　will　simultaneously　serve　sensing　services,　as　well　as　communication　and　computation　services,　e.g.,　FL,　in　future　6G　wireless　networks.　To　this　end,　we　provide　a　novel　integrated　sensing,　communication　and　computation　(ISCC)　system,　called　Fed-ISCC,　where　BS　conducts　sensing　and　FL　in　the　same　time-frequency　resource,　and　the　over-the-air　computation　(AirComp)　is　adopted　to　enable　fast　model　aggregation.　To　mitigate　the　interference　between　sensing　and　FL　during　uplink　transmission,　we　propose　a　receive　beamforming　approach.　Subsequently,　we　analyze　the　convergence　of　FL　in　the　Fed-ISCC　system,　which　reveals　that　the　convergence　of　FL　is　hindered　by　device　selection　error　and　transmission　error　caused　by　sensing　interference,　channel　fading　and　receiver　noise.　Based　on　this　analysis,　we　formulate　an　optimization　problem　that　considers　the　optimization　of　transceiver　beamforming　vectors　and　device　selection　strategy,　with　the　goal　of　minimizing　transmission　and　device　selection　errors　while　ensuring　the　sensing　requirement.　To　address　this　problem,　we　propose　a　joint　optimization　algorithm　that　decouples　it　into　two　main　problems　and　then　solves　them　iteratively.　Simulation　results　demonstrate　that　our　proposed　algorithm　is　superior　to　other　comparison　schemes　and　nearly　attains　the　performance　of　ideal　FL.　IEEE